US20170236307A1 - System and process for color-balancing a series of oblique images - Google Patents
System and process for color-balancing a series of oblique images Download PDFInfo
- Publication number
- US20170236307A1 US20170236307A1 US15/357,490 US201615357490A US2017236307A1 US 20170236307 A1 US20170236307 A1 US 20170236307A1 US 201615357490 A US201615357490 A US 201615357490A US 2017236307 A1 US2017236307 A1 US 2017236307A1
- Authority
- US
- United States
- Prior art keywords
- image
- color
- oblique
- section
- images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000008569 process Effects 0.000 title description 17
- 238000009826 distribution Methods 0.000 claims abstract description 42
- 230000009466 transformation Effects 0.000 claims abstract description 31
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000000844 transformation Methods 0.000 claims description 10
- 238000002156 mixing Methods 0.000 claims description 4
- 230000007423 decrease Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
- H04N1/60—Colour correction or control
- H04N1/6083—Colour correction or control controlled by factors external to the apparatus
-
- G06K9/0063—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/001—Texturing; Colouring; Generation of texture or colour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
- H04N1/60—Colour correction or control
- H04N1/6077—Colour balance, e.g. colour cast correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
Definitions
- imagery is used to capture views of a geographic area and be able to measure objects and structures within the images as well as to be able to determine geographic locations of points within the image.
- These are generally referred to as “geo-referenced images” and come in two basic categories:
- Captured Imagery these images have the appearance as they were captured by the camera or sensor employed.
- the most common form of projected imagery is the ortho-rectified image. This process aligns the image to an orthogonal or rectilinear grid (composed of rectangles).
- the input image used to create an ortho-rectified image is a nadir image—that is, an image captured with the camera pointing straight down.
- oblique image In addition to capturing an image with the camera pointing straight down, it is possible to capture an image with the camera pointing at an oblique angle.
- the resulting imagery is generally referred to as an “oblique image” or as an “oblique aerial image.”
- the capture of oblique aerial images presents additional challenges compared to the capture of nadir images, generally due to the introduction of the oblique angle.
- FIG. 1 An example of a system that captures both nadir and oblique images is shown in FIG. 1 .
- Airplane 10 is flying over the Earth 12 and capturing images utilizing three cameras 14 a , 14 b and 14 c .
- FIG. 1 also illustrates the sun 16 positioned in a northern hemisphere orientation.
- the camera 14 a is shown directed in a southern orientation generally towards the sun 16
- the camera 14 b is shown directed straight down
- the camera 14 c is shown directed in a northern orientation generally away from the sun 16 .
- the cameras 14 a and 14 c capture “oblique images”, while the camera 14 b captures “nadir images”.
- oblique images present a more natural appearance than a nadir image because they show not just the roofs, as is the case of a nadir image, but also the sides of objects and structures. This is what we are most accustomed to seeing.
- oblique images are generally presented without being ortho-rectified and instead left in the natural appearance that the camera captures. This practice makes it very easy for people to look at something in an oblique image and realize what that object is.
- Color balancing nadir aerial images is known in the art.
- color balancing oblique aerial images presents unique challenges.
- nadir images images captured with camera 14 b pointing straight down
- every image has a consistent orientation with respect to the sun 16 .
- oblique images images captured with the cameras 14 a and 14 c pointing at an oblique angle relative to the horizon
- different images have different orientations with respect to the sun 16 . For instance, in the northern hemisphere, a camera aimed to the north (camera 14 c ) points away from the sun 16 , while a camera aimed to the south (camera 14 a ) points toward the sun 16 .
- Specular reflections bounce off a surface and leave the surface at roughly the same angle with which they hit the surface—like a ball bouncing off a flat surface.
- the camera 14 a picks up specular reflections from the sun 16 and therefore any images captured with that camera pick up a strong yellow/red tint to the captured scene.
- the camera 14 c is pointing away from the sun 16 and picks up specular reflections from the sky and therefore any images captured with that camera pick up a strong blue tint to the scene.
- these two images are viewed side by side, the difference can be very noticeable and distracting to the overall image appearance. It is desirable to color balance the oblique images such that they have a substantially consistent color tone.
- FIG. 2 Shown in FIG. 2 is a diagrammatic view of the capturing of three different overlapping images of a same scene from three different positions.
- the three different positions are labeled as Position A, Position B and Position C for purposes of clarity.
- the scene is positioned in the northern hemisphere, and thus, the image captured from Position A is taken with the camera positioned in a southern orientation toward the sun 16 , while the image captured from Position C is taken with the camera positioned in a northern orientation away from the sun 16 .
- the image captured from Position B is taken with the camera positioned directly above the scene.
- the image captured from Position A has a yellow/reddish tint due to the strong specular reflections from the sun 16
- the image captured from Position B has a neutral tint due to roughly equal specular reflections from the sun 16 and sky
- the image captured from Position C has a bluish tint due to the strong specular reflections from the sky.
- FIG. 3 shown therein is a diagrammatic view of the capturing of an oblique image of the Earth 12 where a field of view of the camera is designated with the lines P 1 and P 2 .
- the lines P 1 and P 2 represent path lengths, i.e., the distance the light travels from a scene on the Earth 12 to the camera.
- the path lengths P 1 and P 2 are significantly different and this presents a second challenge to color balancing oblique images: the top of the image goes through significantly more atmosphere than the bottom of the image.
- path length the distance the light must travel from a scene on the Earth 12 to the camera
- lines P 3 and P 4 represent the path lengths for a typical camera/lens configuration, the difference between the shortest path length (straight down) and the longest path length (to the far corner) is only about 6%.
- the path lengths P 1 and P 2 are very different.
- the path length P 1 is infinite—clearly much longer than the path length P 2 at the front of the image.
- the difference between the shortest path length (to the middle front of the image) and the longest path length (to the far back corner of the image) is about 87% -- nearly twice as long.
- FIG. 1 is a diagrammatic view of an airplane flying over the Earth and capturing images utilizing three cameras.
- FIG. 2 is a diagrammatic view of the capturing of three different overlapping images of a same scene from three different positions.
- FIG. 3 is a diagrammatic view of the capturing of an oblique image of the Earth 12 where a field of view of an oblique aerial camera is designated utilizing the path lengths P 1 and P 2 , i.e., the distance the light travels from a scene on the Earth to the camera, and a field of view of a nadir aerial camera is designated utilizing path lengths P 3 and P 4 .
- FIG. 4 is a schematic view of an image processing system constructed in accordance with the present invention.
- FIG. 5 is a schematic view of an oblique image that has been sectioned in accordance with the present invention.
- FIG. 6 is a schematic view of another example of an oblique image that has been sectioned in accordance with the present invention.
- FIG. 7 is a histogram of a color distribution for a red color band of an oblique image in accordance with the present invention.
- FIG. 8 is a histogram of a color distribution of a blue color band of an oblique image in accordance with the present invention.
- FIG. 9 is a portion of a color oblique image captured by a camera angled away from the sun.
- FIG. 10 is a portion of a color oblique image of the same area depicted in FIG. 9 but captured by a camera angled toward the sun.
- the processes described in this patent provide a means for color balancing oblique images so that they take on a consistent color tone.
- the principal behind these processes is to select a set of color-balanced images to use as reference images to create color-balance transformations for a series of oblique images so that the resulting color-balanced oblique images will have a color tone similar to the reference images.
- nadir images are often the best choice for the reference images; however, this is not required. It is recommended that the reference images be from a consistent sun/sky orientation, so, for instance, instead of the nadir images, the north-looking oblique images could be used as the reference images and the remaining oblique images transformed to match their color tone.
- the consistent color tone for the reference images can be achieved in a variety of manners, such as by having images that are naturally balanced, i.e., captured under similar conditions and/or orientations and therefore already have a consistent color tone, or by color-balancing the images to each other after they are captured so that they have a consistent color tone. Or nadir images captured under different conditions can first be color-balanced to each other to produce a consistent color tone.
- color-balancing nadir images described in remote sensing textbooks—basically any method that produces a consistent color tone for a set of similar images will work. As these methods for nadir images are known in the art, they are not discussed here.
- the oblique images can be color balanced to match. This is accomplished by finding one or more portion(s) of reference image(s) that correspond to the same area of the scene contained within the oblique image—in other words, finding their areas of overlap.
- the reference images and the oblique images are geo-referenced so that finding the portions of the references image(s) corresponding to the same area of the scene contained within the oblique images can be accomplished with a computer and thereby automated.
- the logic of the process described herein is executed by a computer to provide an automated process for color-balancing a series of oblique images.
- the image processing system 20 is provided with a computer 22 , and a camera system 24 .
- the image processing system 20 is adapted to color balance the series of oblique images captured from one or more positions and from one or more orientations so that such oblique images are provided with a substantially consistent color balance thereby reducing or even eliminating the unwanted yellowish/reddish or bluish tinting described above.
- the computer 22 receives a series of reference images, and a series of oblique images from the camera system 24 .
- the reference images and the oblique images can be received by the computer system 22 either directly or indirectly from the camera system 24 , and can be passed from the camera system 24 either in batches, in real-time with the capturing of the reference images and/or the oblique images, or at a period of time substantially after the capturing of the reference images and the oblique images.
- the reference images and/or the oblique images can be transmitted or transferred from the camera system 24 to the computer system 22 days and/or weeks and/or years after the capturing of the reference images and the oblique images from the camera system 24 .
- the computer 22 preferably runs image processing software (or firmware) adapted to perform the functions described herein, and the resulting images and data are stored on one or more computer readable mediums.
- Examples of a computer readable medium include an optical storage device, a magnetic storage device, an electronic storage device or the like.
- the term “Computer” as used herein means a system or systems that are able to embody and/or execute the logic of the processes described herein.
- the logic embodied in the form of software instructions or firmware may be executed on any appropriate hardware which may be a dedicated system or systems, or a general purpose computer system, a personal computer system or distributed processing computer system, all of which are well understood in the art, and a detailed description of how to make or use such computer systems is not deemed necessary herein.
- such computer(s) and/or execution can be conducted at a same geographic location or multiple different geographic locations. Furthermore, the execution of the logic can be conducted continuously or at multiple discrete times. Further, such logic can be performed about simultaneously with the capture of the images, or thereafter or combinations thereof.
- the image capture system 24 is typically used for capturing aerial images as shown in FIGS. 1-3 . Suitable image capture systems are shown and described in a provisional patent application identified by U.S. Ser. No. 60/901,444, the entire content of which is hereby incorporated herein by reference. Typically, the image capture system 24 is provided with, one or more image capture devices, one or more monitoring systems, one or more event multiplexer systems, and one or more data storage units or computer systems. In the examples depicted in FIGS. 1-3 of U.S. Ser. No. 60/901,444, the “image capture system 10 ” is provided with four image capture devices mounted in a sweep pattern (see FIG. 1 of U.S. Ser. No.
- 60/901,444 five image capture devices mounted in a 360 pattern having image capture devices pointing fore, aft, port, starboard and straight down (see FIG. 2 of U.S. Ser. No. 60/901,444); or four image capture devices mounted in separate directions generally aligned with respective parts of streets (see FIG. 3 of U.S. Ser. No. 60/901,444).
- the image capture devices of the image capture system 24 can be mounted to a moving platform such as a manned airplane, an unmanned airplane, a train, an automobile such as a van, a boat, a four wheeler, a motor cycle, a tractor, a robotic device or the like.
- a moving platform such as a manned airplane, an unmanned airplane, a train, an automobile such as a van, a boat, a four wheeler, a motor cycle, a tractor, a robotic device or the like.
- the computer 22 executes instructions to effect the color-balancing of the series of oblique images captured from one or more positions and from one or more orientations.
- the computer 22 is programmed with instructions to locate one or more portions of one or more reference images that overlap the oblique image, and then create a color balancing transformation that approximately matches the color distribution of the oblique image to the color distribution of the overlapping portions of the reference images.
- the computer 22 transforms pixels in the oblique image according to the color balancing transformation created for that oblique image, and then preferably stores the transform pixel values in the oblique image or a copy of the oblique image.
- the oblique images having the transformed pixel values are referred to herein after as “color-balanced oblique images”.
- the reference images are geo-referenced to aid in the location of the overlapping portion(s), and also color-balanced.
- the reference images can be color-balanced either naturally because they are captured from a consistent orientation, or they can be color-balanced using well-known practices.
- the reference images are nadir images.
- the overlapping portions of the reference images and the oblique images have a similar scene -because it is expected that the scenes will be somewhat different. For example, assuming that the scene includes a building, the oblique images will show the sides of the building while the nadir images will not. Typically, the closer the scene contents in the overlapping portion(s) match (Leaf-on, leaf off, flooding, snow, or the like) the better the results. Ideally, the reference images and the oblique images will be taken during the same photo shoot to enhance the similarity of the lighting and scene content.
- one or more color balancing transformation is created for each of the oblique images in the series of oblique images.
- the one or more color balancing transformations do not have to be made for each of the oblique images in the series. In other words, not all of the oblique images in the series of oblique images must be color-balanced in accordance with the present invention.
- all of the pixels in the oblique image are preferably transformed according to the one or more color balancing transformation created for that particular oblique image, it should be understood that less than all of the pixels can be transformed. For example, the pixels in the oblique image can be organized into groups, and then a certain percentage of such pixels (such as 60-90%) can be transformed.
- the automated process preferably (1) divides each oblique image in the series into a plurality of sections, (2) identifies a portion of a reference image overlapping the section, and then (3) creates a color-balancing transformation.
- a color-balancing transformation for each color band in the color space is created and for each section in the oblique image approximating the color distribution of the overlapping section in the one or more reference images.
- a histogram of the color distribution for each color band i.e., red, green and blue in each section of the oblique image and the overlapping portion of the same scene in the nadir image (develop two histograms for each section) is created.
- Exemplary histograms for the red and blue color bands are shown in FIGS. 7 and 8 .
- the color space can be any suitable color space, such as RGB, XYZ, LUV, CMYK, or false color IR.
- the color distribution histogram of an image shows the number of pixels for each pixel value within the range of the image. If the minimum value of the image is 0 and the maximum value of the image is 255, the histogram of the image shows the number of pixels for each value ranging between and including 0 and 255. Peaks in the histogram represent more common values within the image that usually consist of nearly uniform regions. Valleys in the histogram represent less common values. Empty regions within the histogram indicate that no pixels within the image contain those values.
- FIG. 7 and 8 show exemplary values of an aerial oblique image that has not yet been color-balanced while the dashed lines shown in the histograms show exemplary values of the same aerial oblique image that has been color-balanced utilizing the process described herein.
- the solid line in the histogram of FIG. 7 shows the color distribution of the red band for an oblique image that was captured by a camera pointing in a generally south direction and that has a reddish tint due to the specular reflections from the sun 16
- the dashed line shows a reduction in the red pixel values in the color-balanced image
- the solid line in the histogram of FIG. 8 shows the color distribution of the blue band for an oblique image that was captured by a camera pointing in a generally north direction and that has a bluish tint due to the specular reflections from the sky.
- the dashed line in FIG. 8 shows a reduction in the blue pixel values in the color-balanced image.
- each of the oblique images are preferably divided into a plurality of sections.
- an oblique image 30 shown in FIG. 5 has been divided into nine sections 32 a - i
- the oblique image 34 shown in FIG. 6 has been divided into 6 sections 36 a - f.
- Any number of sections can be used, but dividing the oblique image 30 into more sections decreases the likelihood of variability in the image.
- oblique images can change color depending upon their orientation and the distance that the scene is away from the camera. Images taken in a direction away from the sun 16 are usually bluer at the top, while images taken in a direction toward the sun 16 have a reddish-orange cast.
- the number, size and location of the sections within the oblique images can be predetermined or randomly determined.
- a color-balancing transformation is created that approximately matches the color distribution of the oblique image section to the color distribution of the overlapping reference portion(s).
- Histogram equalization is a well known algorithm, so no further comments are deemed necessary to teach one skilled in the art how to make and use histogram equalization. For the oblique image 30 that has been divided into nine oblique image sections 32 a - i, this process occurs nine times.
- At least three histograms are created for each color band in the color space.
- pixel values for each color band in each of the oblique images are color-balanced and blended to provide a substantially consistent color tone.
- This can be accomplished by using a combination of the color-balancing transformations (e.g., histograms) for the oblique image.
- the blending may be accomplished through bi-linear interpolation, linear interpolation, cubics, splines and/or the like. Alternatively, one transform may be used for the entire image.
- the color-balancing and blending is accomplished as follows. First, on a pixel by pixel basis, for the oblique image to be color-balanced, one or more oblique image sections are selected which apply to the particular pixel. Then, for each color band, the pixel value is calculated independently (or transformed) using the color balancing transformation for each selected oblique image section yielding a transformed pixel value for each selected oblique image section. Then, the transformed pixel values are blended into a single resulting pixel value using any suitable algorithm, such as bi-linear interpolation, linear interpolation, cubics, splines or the like.
- the resulting pixel value is stored in the oblique image or a copy (such as a memory copy) of the oblique image.
- This process is preferably repeated for every pixel in the oblique image. However, it should be understood that this process could only be repeated for a subset of the pixels.
- the process described above may be performed on a continuous or intermittent basis.
- section color balancing transformations can be stored, and then applied on a pixel by pixel basis at a later time to color-balance the oblique image.
- the color-balancing transformations can be stored with the oblique image and then utilized to color-balance the oblique image when it is retrieved or displayed.
- RGB color images the above process is repeated three times, once for each color pixel component, i.e. the red pixels, the green pixels, and the blue pixels, each with its own color-balancing transformation.
Abstract
Description
- This application is a continuation of U.S. Ser. No. 14/632,732, filed Feb. 26, 2015, which is a continuation of Ser. No. 14/153,772, filed Jan. 13, 2014, which is a continuation of U.S. Ser. No. 13/181,259, filed Jul. 12, 2011, now patent number U.S. Pat. No. 8,649,596 issued Feb. 11, 2014, which is a continuation of U.S. Ser. No. 11/871,740, filed on Oct. 12, 2007, now patent number U.S. Pat. No. 7,991,226 issued Aug. 2, 2011, the entire contents of all of which are hereby expressly incorporated herein by reference.
- Not Applicable.
- Not Applicable.
- Not Applicable.
- In the remote sensing/aerial imaging industry, imagery is used to capture views of a geographic area and be able to measure objects and structures within the images as well as to be able to determine geographic locations of points within the image. These are generally referred to as “geo-referenced images” and come in two basic categories:
- 1. Captured Imagery—these images have the appearance as they were captured by the camera or sensor employed.
- 2. Projected Imagery—these images have been processed and converted such that they confirm to a mathematical projection.
- All imagery starts as captured imagery, but as most software cannot geo-reference captured imagery, that imagery is then reprocessed to create the projected imagery. The most common form of projected imagery is the ortho-rectified image. This process aligns the image to an orthogonal or rectilinear grid (composed of rectangles). The input image used to create an ortho-rectified image is a nadir image—that is, an image captured with the camera pointing straight down.
- In addition to capturing an image with the camera pointing straight down, it is possible to capture an image with the camera pointing at an oblique angle. The resulting imagery is generally referred to as an “oblique image” or as an “oblique aerial image.” The capture of oblique aerial images presents additional challenges compared to the capture of nadir images, generally due to the introduction of the oblique angle.
- An example of a system that captures both nadir and oblique images is shown in
FIG. 1 .Airplane 10 is flying over the Earth 12 and capturing images utilizing threecameras 14 a, 14 b and 14 c.FIG. 1 also illustrates thesun 16 positioned in a northern hemisphere orientation. The camera 14 a is shown directed in a southern orientation generally towards thesun 16, the camera 14 b is shown directed straight down, and thecamera 14 c is shown directed in a northern orientation generally away from thesun 16. Thecameras 14 a and 14 c capture “oblique images”, while the camera 14 b captures “nadir images”. - The oblique images present a more natural appearance than a nadir image because they show not just the roofs, as is the case of a nadir image, but also the sides of objects and structures. This is what we are most accustomed to seeing. In order to preserve this natural perspective, oblique images are generally presented without being ortho-rectified and instead left in the natural appearance that the camera captures. This practice makes it very easy for people to look at something in an oblique image and realize what that object is.
- However, the sun/sky orientation when an oblique image is taken has a major impact on the color balance of the resulting photograph due to the reflections of light from the
sun 16. There are two major types of reflection: diffuse and specular. Flat wall paint is a highly diffuse reflector—that is, light bounces nearly equally in all directions. A mirror is a highly specular reflector—that is, light bounces almost entirely in one direction off the mirror. There is nothing in nature that is a perfect specular or a perfect diffuse reflector—everything is some combination of the two. It is the specular nature of objects that presents a problem for color balancing oblique images. - Color balancing nadir aerial images is known in the art. However, color balancing oblique aerial images presents unique challenges. When collecting nadir images (images captured with camera 14 b pointing straight down), every image has a consistent orientation with respect to the
sun 16. However, when collecting oblique images (images captured with thecameras 14 a and 14 c pointing at an oblique angle relative to the horizon) different images have different orientations with respect to thesun 16. For instance, in the northern hemisphere, a camera aimed to the north (camera 14 c ) points away from thesun 16, while a camera aimed to the south (camera 14 a ) points toward thesun 16. - Specular reflections bounce off a surface and leave the surface at roughly the same angle with which they hit the surface—like a ball bouncing off a flat surface. When the camera 14 a is pointing towards the
sun 16, the camera 14 a picks up specular reflections from thesun 16 and therefore any images captured with that camera pick up a strong yellow/red tint to the captured scene. Thecamera 14 c, on the other hand, is pointing away from thesun 16 and picks up specular reflections from the sky and therefore any images captured with that camera pick up a strong blue tint to the scene. When these two images are viewed side by side, the difference can be very noticeable and distracting to the overall image appearance. It is desirable to color balance the oblique images such that they have a substantially consistent color tone. - Shown in
FIG. 2 is a diagrammatic view of the capturing of three different overlapping images of a same scene from three different positions. The three different positions are labeled as Position A, Position B and Position C for purposes of clarity. The scene is positioned in the northern hemisphere, and thus, the image captured from Position A is taken with the camera positioned in a southern orientation toward thesun 16, while the image captured from Position C is taken with the camera positioned in a northern orientation away from thesun 16. The image captured from Position B is taken with the camera positioned directly above the scene. In this example, the image captured from Position A has a yellow/reddish tint due to the strong specular reflections from thesun 16, the image captured from Position B has a neutral tint due to roughly equal specular reflections from thesun 16 and sky, and the image captured from Position C has a bluish tint due to the strong specular reflections from the sky. - Referring to
FIG. 3 , shown therein is a diagrammatic view of the capturing of an oblique image of the Earth 12 where a field of view of the camera is designated with the lines P1 and P2. The lines P1 and P2 represent path lengths, i.e., the distance the light travels from a scene on the Earth 12 to the camera. In an oblique image, the path lengths P1 and P2 are significantly different and this presents a second challenge to color balancing oblique images: the top of the image goes through significantly more atmosphere than the bottom of the image. In a nadir image, path length (the distance the light must travel from a scene on the Earth 12 to the camera) at the edges of the useable image are typically not all that much different than the path length to the nadir point. For instance, lines P3 and P4 represent the path lengths for a typical camera/lens configuration, the difference between the shortest path length (straight down) and the longest path length (to the far corner) is only about 6%. - But with oblique images, because of the nature of trigonometry, when the field of view angle is added to the oblique camera axis angle, the path lengths P1 and P2 are very different. To illustrate an extreme, if the top of the camera is pointed above the horizon then the path length P1 is infinite—clearly much longer than the path length P2 at the front of the image. In a typical camera/lens configuration and at a typical oblique angle, the difference between the shortest path length (to the middle front of the image) and the longest path length (to the far back corner of the image) is about 87% -- nearly twice as long.
- The challenge this difference in path length presents is that the light from the scene captured by the top of the camera travels through a lot more atmosphere than the light from the scene captured by the bottom of the camera. This results in more tinting or scattering, an increased introduction of blue sky light, an increase in blurriness, and a decrease in clarity due to smog or haze. Thus, if the image is color balanced based upon the tinting in the top of the image then the color balancing of the bottom of the image will be incorrect. Likewise, if the image is color-balanced based upon the tinting in the bottom of the image then the color-balancing of the top of the image will be incorrect. One could color-balance based upon the tinting in the middle of the image, but then the color-balancing of the top and bottom of the image would be incorrect.
- In light of the foregoing, there is a need for a system and process for color-balancing oblique images that overcomes the challenges discussed above. It is to such a system and process that the present invention is directed.
- So that the above recited features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof that are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
- This patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
-
FIG. 1 is a diagrammatic view of an airplane flying over the Earth and capturing images utilizing three cameras. -
FIG. 2 is a diagrammatic view of the capturing of three different overlapping images of a same scene from three different positions. -
FIG. 3 is a diagrammatic view of the capturing of an oblique image of theEarth 12 where a field of view of an oblique aerial camera is designated utilizing the path lengths P1 and P2, i.e., the distance the light travels from a scene on the Earth to the camera, and a field of view of a nadir aerial camera is designated utilizing path lengths P3 and P4. -
FIG. 4 is a schematic view of an image processing system constructed in accordance with the present invention. -
FIG. 5 is a schematic view of an oblique image that has been sectioned in accordance with the present invention. -
FIG. 6 is a schematic view of another example of an oblique image that has been sectioned in accordance with the present invention. -
FIG. 7 is a histogram of a color distribution for a red color band of an oblique image in accordance with the present invention. -
FIG. 8 is a histogram of a color distribution of a blue color band of an oblique image in accordance with the present invention. -
FIG. 9 is a portion of a color oblique image captured by a camera angled away from the sun. -
FIG. 10 is a portion of a color oblique image of the same area depicted inFIG. 9 but captured by a camera angled toward the sun. - Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction, experiments, exemplary data, and/or the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for purpose of description and should not be regarded as limiting.
- The processes described in this patent provide a means for color balancing oblique images so that they take on a consistent color tone. The principal behind these processes is to select a set of color-balanced images to use as reference images to create color-balance transformations for a series of oblique images so that the resulting color-balanced oblique images will have a color tone similar to the reference images. Because they typically have a consistent sun/sky orientation, nadir images are often the best choice for the reference images; however, this is not required. It is recommended that the reference images be from a consistent sun/sky orientation, so, for instance, instead of the nadir images, the north-looking oblique images could be used as the reference images and the remaining oblique images transformed to match their color tone.
- The consistent color tone for the reference images can be achieved in a variety of manners, such as by having images that are naturally balanced, i.e., captured under similar conditions and/or orientations and therefore already have a consistent color tone, or by color-balancing the images to each other after they are captured so that they have a consistent color tone. Or nadir images captured under different conditions can first be color-balanced to each other to produce a consistent color tone. There are numerous methods for color-balancing nadir images described in remote sensing textbooks—basically any method that produces a consistent color tone for a set of similar images will work. As these methods for nadir images are known in the art, they are not discussed here.
- Once the reference images have been selected, the oblique images can be color balanced to match. This is accomplished by finding one or more portion(s) of reference image(s) that correspond to the same area of the scene contained within the oblique image—in other words, finding their areas of overlap.
- In a preferred embodiment, the reference images and the oblique images are geo-referenced so that finding the portions of the references image(s) corresponding to the same area of the scene contained within the oblique images can be accomplished with a computer and thereby automated.
- Thus, in the preferred embodiment, the logic of the process described herein is executed by a computer to provide an automated process for color-balancing a series of oblique images.
- Referring now to the drawings, and in particular to
FIG. 4 , shown therein and designated by areference numeral 20 is an image processing system constructed in accordance with the present invention. In general, theimage processing system 20 is provided with acomputer 22, and acamera system 24. As will be described in more detail below, theimage processing system 20 is adapted to color balance the series of oblique images captured from one or more positions and from one or more orientations so that such oblique images are provided with a substantially consistent color balance thereby reducing or even eliminating the unwanted yellowish/reddish or bluish tinting described above. - In general, the
computer 22 receives a series of reference images, and a series of oblique images from thecamera system 24. The reference images and the oblique images can be received by thecomputer system 22 either directly or indirectly from thecamera system 24, and can be passed from thecamera system 24 either in batches, in real-time with the capturing of the reference images and/or the oblique images, or at a period of time substantially after the capturing of the reference images and the oblique images. For example, the reference images and/or the oblique images can be transmitted or transferred from thecamera system 24 to thecomputer system 22 days and/or weeks and/or years after the capturing of the reference images and the oblique images from thecamera system 24. - The
computer 22 preferably runs image processing software (or firmware) adapted to perform the functions described herein, and the resulting images and data are stored on one or more computer readable mediums. Examples of a computer readable medium include an optical storage device, a magnetic storage device, an electronic storage device or the like. The term “Computer” as used herein means a system or systems that are able to embody and/or execute the logic of the processes described herein. The logic embodied in the form of software instructions or firmware may be executed on any appropriate hardware which may be a dedicated system or systems, or a general purpose computer system, a personal computer system or distributed processing computer system, all of which are well understood in the art, and a detailed description of how to make or use such computer systems is not deemed necessary herein. When thecomputer 22 is used to execute the logic of the processes described herein, such computer(s) and/or execution can be conducted at a same geographic location or multiple different geographic locations. Furthermore, the execution of the logic can be conducted continuously or at multiple discrete times. Further, such logic can be performed about simultaneously with the capture of the images, or thereafter or combinations thereof. - The
image capture system 24 is typically used for capturing aerial images as shown inFIGS. 1-3 . Suitable image capture systems are shown and described in a provisional patent application identified by U.S. Ser. No. 60/901,444, the entire content of which is hereby incorporated herein by reference. Typically, theimage capture system 24 is provided with, one or more image capture devices, one or more monitoring systems, one or more event multiplexer systems, and one or more data storage units or computer systems. In the examples depicted in FIGS. 1-3 of U.S. Ser. No. 60/901,444, the “image capture system 10” is provided with four image capture devices mounted in a sweep pattern (see FIG. 1 of U.S. Ser. No. 60/901,444); five image capture devices mounted in a 360 pattern having image capture devices pointing fore, aft, port, starboard and straight down (see FIG. 2 of U.S. Ser. No. 60/901,444); or four image capture devices mounted in separate directions generally aligned with respective parts of streets (see FIG. 3 of U.S. Ser. No. 60/901,444). - In certain embodiments, the image capture devices of the
image capture system 24 can be mounted to a moving platform such as a manned airplane, an unmanned airplane, a train, an automobile such as a van, a boat, a four wheeler, a motor cycle, a tractor, a robotic device or the like. - As discussed above, the
computer 22 executes instructions to effect the color-balancing of the series of oblique images captured from one or more positions and from one or more orientations. On an oblique image by oblique image basis, thecomputer 22 is programmed with instructions to locate one or more portions of one or more reference images that overlap the oblique image, and then create a color balancing transformation that approximately matches the color distribution of the oblique image to the color distribution of the overlapping portions of the reference images. Then, thecomputer 22 transforms pixels in the oblique image according to the color balancing transformation created for that oblique image, and then preferably stores the transform pixel values in the oblique image or a copy of the oblique image. The oblique images having the transformed pixel values are referred to herein after as “color-balanced oblique images”. - In a preferred embodiment, the reference images are geo-referenced to aid in the location of the overlapping portion(s), and also color-balanced. The reference images can be color-balanced either naturally because they are captured from a consistent orientation, or they can be color-balanced using well-known practices. In a preferred embodiment, the reference images are nadir images.
- In a preferred embodiment, the overlapping portions of the reference images and the oblique images have a similar scene -because it is expected that the scenes will be somewhat different. For example, assuming that the scene includes a building, the oblique images will show the sides of the building while the nadir images will not. Typically, the closer the scene contents in the overlapping portion(s) match (Leaf-on, leaf off, flooding, snow, or the like) the better the results. Ideally, the reference images and the oblique images will be taken during the same photo shoot to enhance the similarity of the lighting and scene content.
- Preferably, one or more color balancing transformation is created for each of the oblique images in the series of oblique images. However, it should be understood that the one or more color balancing transformations do not have to be made for each of the oblique images in the series. In other words, not all of the oblique images in the series of oblique images must be color-balanced in accordance with the present invention. In addition, while all of the pixels in the oblique image are preferably transformed according to the one or more color balancing transformation created for that particular oblique image, it should be understood that less than all of the pixels can be transformed. For example, the pixels in the oblique image can be organized into groups, and then a certain percentage of such pixels (such as 60-90%) can be transformed.
- In general, the automated process preferably (1) divides each oblique image in the series into a plurality of sections, (2) identifies a portion of a reference image overlapping the section, and then (3) creates a color-balancing transformation. Preferably a color-balancing transformation for each color band in the color space is created and for each section in the oblique image approximating the color distribution of the overlapping section in the one or more reference images. For example, assuming an RGB color space, a histogram of the color distribution for each color band, i.e., red, green and blue in each section of the oblique image and the overlapping portion of the same scene in the nadir image (develop two histograms for each section) is created. Exemplary histograms for the red and blue color bands are shown in
FIGS. 7 and 8 . The color space can be any suitable color space, such as RGB, XYZ, LUV, CMYK, or false color IR. - The color distribution histogram of an image shows the number of pixels for each pixel value within the range of the image. If the minimum value of the image is 0 and the maximum value of the image is 255, the histogram of the image shows the number of pixels for each value ranging between and including 0 and 255. Peaks in the histogram represent more common values within the image that usually consist of nearly uniform regions. Valleys in the histogram represent less common values. Empty regions within the histogram indicate that no pixels within the image contain those values. The solid lines shown in the histograms in
FIGS. 7 and 8 show exemplary values of an aerial oblique image that has not yet been color-balanced while the dashed lines shown in the histograms show exemplary values of the same aerial oblique image that has been color-balanced utilizing the process described herein. - The solid line in the histogram of
FIG. 7 shows the color distribution of the red band for an oblique image that was captured by a camera pointing in a generally south direction and that has a reddish tint due to the specular reflections from thesun 16, while the dashed line shows a reduction in the red pixel values in the color-balanced image. Similarly, the solid line in the histogram ofFIG. 8 shows the color distribution of the blue band for an oblique image that was captured by a camera pointing in a generally north direction and that has a bluish tint due to the specular reflections from the sky. The dashed line inFIG. 8 shows a reduction in the blue pixel values in the color-balanced image. - To color-balance the series of oblique images, each of the oblique images are preferably divided into a plurality of sections. For example, an
oblique image 30 shown inFIG. 5 has been divided into nine sections 32 a-i, and theoblique image 34 shown inFIG. 6 has been divided into 6 sections 36 a-f. Any number of sections can be used, but dividing theoblique image 30 into more sections decreases the likelihood of variability in the image. For example, oblique images can change color depending upon their orientation and the distance that the scene is away from the camera. Images taken in a direction away from thesun 16 are usually bluer at the top, while images taken in a direction toward thesun 16 have a reddish-orange cast. The number, size and location of the sections within the oblique images can be predetermined or randomly determined. - Once the oblique image has been divided into sections, then, on a section by section basis one or more portions of a reference image is located that overlaps the oblique image section. Then, a color-balancing transformation is created that approximately matches the color distribution of the oblique image section to the color distribution of the overlapping reference portion(s). This can be accomplished using any suitable algorithm or technique, such as histogram equalization. Histogram equalization is a well known algorithm, so no further comments are deemed necessary to teach one skilled in the art how to make and use histogram equalization. For the
oblique image 30 that has been divided into nine oblique image sections 32 a-i, this process occurs nine times. - For each section, at least three histograms (color distribution for the overlapping reference portion, color distribution for the oblique image section, and color balancing transformation for the oblique image section) are created for each color band in the color space.
- Then, pixel values for each color band in each of the oblique images are color-balanced and blended to provide a substantially consistent color tone. This can be accomplished by using a combination of the color-balancing transformations (e.g., histograms) for the oblique image. The blending may be accomplished through bi-linear interpolation, linear interpolation, cubics, splines and/or the like. Alternatively, one transform may be used for the entire image.
- In a preferred embodiment, the color-balancing and blending is accomplished as follows. First, on a pixel by pixel basis, for the oblique image to be color-balanced, one or more oblique image sections are selected which apply to the particular pixel. Then, for each color band, the pixel value is calculated independently (or transformed) using the color balancing transformation for each selected oblique image section yielding a transformed pixel value for each selected oblique image section. Then, the transformed pixel values are blended into a single resulting pixel value using any suitable algorithm, such as bi-linear interpolation, linear interpolation, cubics, splines or the like. Then, the resulting pixel value is stored in the oblique image or a copy (such as a memory copy) of the oblique image. This process is preferably repeated for every pixel in the oblique image. However, it should be understood that this process could only be repeated for a subset of the pixels.
- In general, the process described above may be performed on a continuous or intermittent basis. For example, once the section color balancing transformations are created, such section color balancing transformations can be stored, and then applied on a pixel by pixel basis at a later time to color-balance the oblique image. For example, the color-balancing transformations can be stored with the oblique image and then utilized to color-balance the oblique image when it is retrieved or displayed.
- Set forth hereinafter is pseudo-code for one embodiment of the present invention:
-
Select oblique images to adjust Select reference images For( each image to be color-balanced ){ Divide image into sections For( each image section used ){ Choose one or more overlapping reference images If ( only one reference image is chosen ) { Create section color-balancing transformation from chosen reference image } Else { Create empty section color-balancing transformation For ( each chosen reference image ){ Create temporary color-balancing transformation for this chosen reference image Combine temporary color-balancing transformation into section color-balancing transformation } } } For (each pixel to be transformed){ Create empty final pixel value For (each image section used){ If (section is applicable to pixel){ Compute transformed pixel value from section color- balancing transformation Blend transformed pixel value into final pixel value } } Store final pixel value } } - For RGB color images, the above process is repeated three times, once for each color pixel component, i.e. the red pixels, the green pixels, and the blue pixels, each with its own color-balancing transformation.
- Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be obvious to those skilled in the art that certain changes and modifications may be practiced without departing from the spirit and scope thereof, as described in this specification and as defined in the appended claims below. The term “comprising” within the claims is intended to mean “including at least” such that the recited listing of elements in a claim are an open group. “A,” “an” and other singular terms are intended to include the plural forms thereof unless specifically excluded.
Claims (10)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/357,490 US20170236307A1 (en) | 2007-10-12 | 2016-11-21 | System and process for color-balancing a series of oblique images |
US16/191,232 US10580169B2 (en) | 2007-10-12 | 2018-11-14 | System and process for color-balancing a series of oblique images |
US16/806,347 US11087506B2 (en) | 2007-10-12 | 2020-03-02 | System and process for color-balancing a series of oblique images |
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/871,740 US7991226B2 (en) | 2007-10-12 | 2007-10-12 | System and process for color-balancing a series of oblique images |
US13/181,259 US8649596B2 (en) | 2007-10-12 | 2011-07-12 | System and process for color-balancing a series of oblique images |
US14/153,772 US8971624B2 (en) | 2007-10-12 | 2014-01-13 | System and process for color-balancing a series of oblique images |
US14/632,732 US9503615B2 (en) | 2007-10-12 | 2015-02-26 | System and process for color-balancing a series of oblique images |
US15/357,490 US20170236307A1 (en) | 2007-10-12 | 2016-11-21 | System and process for color-balancing a series of oblique images |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/632,732 Continuation US9503615B2 (en) | 2007-10-12 | 2015-02-26 | System and process for color-balancing a series of oblique images |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/191,232 Continuation US10580169B2 (en) | 2007-10-12 | 2018-11-14 | System and process for color-balancing a series of oblique images |
Publications (1)
Publication Number | Publication Date |
---|---|
US20170236307A1 true US20170236307A1 (en) | 2017-08-17 |
Family
ID=40534256
Family Applications (7)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/871,740 Expired - Fee Related US7991226B2 (en) | 2007-10-12 | 2007-10-12 | System and process for color-balancing a series of oblique images |
US13/181,259 Active 2028-04-30 US8649596B2 (en) | 2007-10-12 | 2011-07-12 | System and process for color-balancing a series of oblique images |
US14/153,772 Active US8971624B2 (en) | 2007-10-12 | 2014-01-13 | System and process for color-balancing a series of oblique images |
US14/632,732 Active 2028-08-18 US9503615B2 (en) | 2007-10-12 | 2015-02-26 | System and process for color-balancing a series of oblique images |
US15/357,490 Abandoned US20170236307A1 (en) | 2007-10-12 | 2016-11-21 | System and process for color-balancing a series of oblique images |
US16/191,232 Active US10580169B2 (en) | 2007-10-12 | 2018-11-14 | System and process for color-balancing a series of oblique images |
US16/806,347 Active US11087506B2 (en) | 2007-10-12 | 2020-03-02 | System and process for color-balancing a series of oblique images |
Family Applications Before (4)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/871,740 Expired - Fee Related US7991226B2 (en) | 2007-10-12 | 2007-10-12 | System and process for color-balancing a series of oblique images |
US13/181,259 Active 2028-04-30 US8649596B2 (en) | 2007-10-12 | 2011-07-12 | System and process for color-balancing a series of oblique images |
US14/153,772 Active US8971624B2 (en) | 2007-10-12 | 2014-01-13 | System and process for color-balancing a series of oblique images |
US14/632,732 Active 2028-08-18 US9503615B2 (en) | 2007-10-12 | 2015-02-26 | System and process for color-balancing a series of oblique images |
Family Applications After (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/191,232 Active US10580169B2 (en) | 2007-10-12 | 2018-11-14 | System and process for color-balancing a series of oblique images |
US16/806,347 Active US11087506B2 (en) | 2007-10-12 | 2020-03-02 | System and process for color-balancing a series of oblique images |
Country Status (6)
Country | Link |
---|---|
US (7) | US7991226B2 (en) |
EP (1) | EP2210212A4 (en) |
AU (1) | AU2008310726B2 (en) |
BR (1) | BRPI0818059A2 (en) |
CA (1) | CA2702258C (en) |
WO (1) | WO2009049151A1 (en) |
Families Citing this family (46)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8078436B2 (en) | 2007-04-17 | 2011-12-13 | Eagle View Technologies, Inc. | Aerial roof estimation systems and methods |
US8145578B2 (en) | 2007-04-17 | 2012-03-27 | Eagel View Technologies, Inc. | Aerial roof estimation system and method |
EP3056836A3 (en) | 2007-10-04 | 2016-09-14 | Sungevity | System and method for provisioning solar energy systems |
US7991226B2 (en) * | 2007-10-12 | 2011-08-02 | Pictometry International Corporation | System and process for color-balancing a series of oblique images |
US8417061B2 (en) * | 2008-02-01 | 2013-04-09 | Sungevity Inc. | Methods and systems for provisioning energy systems |
US8675068B2 (en) * | 2008-04-11 | 2014-03-18 | Nearmap Australia Pty Ltd | Systems and methods of capturing large area images in detail including cascaded cameras and/or calibration features |
US8497905B2 (en) * | 2008-04-11 | 2013-07-30 | nearmap australia pty ltd. | Systems and methods of capturing large area images in detail including cascaded cameras and/or calibration features |
US8731234B1 (en) | 2008-10-31 | 2014-05-20 | Eagle View Technologies, Inc. | Automated roof identification systems and methods |
US8170840B2 (en) | 2008-10-31 | 2012-05-01 | Eagle View Technologies, Inc. | Pitch determination systems and methods for aerial roof estimation |
US8209152B2 (en) * | 2008-10-31 | 2012-06-26 | Eagleview Technologies, Inc. | Concurrent display systems and methods for aerial roof estimation |
JP5288482B2 (en) * | 2009-07-21 | 2013-09-11 | Nec東芝スペースシステム株式会社 | Imaging apparatus, imaging method, imaging circuit, and program |
CA2801486C (en) | 2010-02-01 | 2018-12-11 | Eagle View Technologies, Inc. | Geometric correction of rough wireframe models derived from photographs |
US8374428B2 (en) * | 2010-12-05 | 2013-02-12 | Microsoft Corporation | Color balancing for partially overlapping images |
WO2012148919A2 (en) * | 2011-04-25 | 2012-11-01 | Skybox Imaging, Inc. | Systems and methods for overhead imaging and video |
JP5882693B2 (en) * | 2011-11-24 | 2016-03-09 | 株式会社トプコン | Aerial photography imaging method and aerial photography imaging apparatus |
US8970691B2 (en) | 2011-08-26 | 2015-03-03 | Microsoft Technology Licensing, Llc | Removal of rayleigh scattering from images |
US9599466B2 (en) | 2012-02-03 | 2017-03-21 | Eagle View Technologies, Inc. | Systems and methods for estimation of building wall area |
US10663294B2 (en) | 2012-02-03 | 2020-05-26 | Eagle View Technologies, Inc. | Systems and methods for estimation of building wall area and producing a wall estimation report |
US8774525B2 (en) | 2012-02-03 | 2014-07-08 | Eagle View Technologies, Inc. | Systems and methods for estimation of building floor area |
US9933257B2 (en) | 2012-02-03 | 2018-04-03 | Eagle View Technologies, Inc. | Systems and methods for estimation of building wall area |
US10515414B2 (en) | 2012-02-03 | 2019-12-24 | Eagle View Technologies, Inc. | Systems and methods for performing a risk management assessment of a property |
US9501700B2 (en) | 2012-02-15 | 2016-11-22 | Xactware Solutions, Inc. | System and method for construction estimation using aerial images |
US11587176B2 (en) | 2013-03-15 | 2023-02-21 | Eagle View Technologies, Inc. | Price estimation model |
US10909482B2 (en) | 2013-03-15 | 2021-02-02 | Pictometry International Corp. | Building materials estimation |
US9959581B2 (en) | 2013-03-15 | 2018-05-01 | Eagle View Technologies, Inc. | Property management on a smartphone |
EP2787319A1 (en) * | 2013-04-05 | 2014-10-08 | Leica Geosystems AG | Control of an image triggering system for taking aerial photographs in nadir alignment for an unmanned aircraft |
US20140300736A1 (en) * | 2013-04-09 | 2014-10-09 | Microsoft Corporation | Multi-sensor camera recalibration |
WO2014184244A1 (en) * | 2013-05-16 | 2014-11-20 | Thomson Licensing | Method for transfering the chromaticity of an example-image to the chromaticity of an image |
EP3028464B1 (en) | 2013-08-02 | 2019-05-01 | Xactware Solutions Inc. | System and method for detecting features in aerial images using disparity mapping and segmentation techniques |
CN105917337A (en) | 2013-08-29 | 2016-08-31 | 桑格威迪公司 | Improving designing and installation quoting for solar energy systems |
US10230925B2 (en) | 2014-06-13 | 2019-03-12 | Urthecast Corp. | Systems and methods for processing and providing terrestrial and/or space-based earth observation video |
US9734599B2 (en) | 2014-10-08 | 2017-08-15 | Microsoft Technology Licensing, Llc | Cross-level image blending |
US9460517B2 (en) | 2014-10-22 | 2016-10-04 | Pointivo, Inc | Photogrammetric methods and devices related thereto |
US10871561B2 (en) | 2015-03-25 | 2020-12-22 | Urthecast Corp. | Apparatus and methods for synthetic aperture radar with digital beamforming |
AU2016201867B2 (en) * | 2015-03-27 | 2017-09-28 | Konica Minolta Laboratory U.S.A., Inc. | Method and system to avoid plant shadows for vegetation and soil imaging |
EP3311449B1 (en) | 2015-06-16 | 2019-12-11 | King Abdulaziz City for Science and Technology | Efficient planar phased array antenna assembly |
CA3044806A1 (en) | 2015-11-25 | 2017-06-01 | Urthecast Corp. | Synthetic aperture radar imaging apparatus and methods |
CN105698762B (en) * | 2016-01-15 | 2018-02-23 | 中国人民解放军国防科学技术大学 | Target method for rapidly positioning based on observation station at different moments on a kind of unit flight path |
US10628802B2 (en) | 2016-05-19 | 2020-04-21 | Lockheed Martin Corporation | Systems and methods for assessing damage to infrastructure assets |
US10032267B2 (en) | 2016-06-09 | 2018-07-24 | Lockheed Martin Corporation | Automating the assessment of damage to infrastructure assets |
CA3064735C (en) | 2017-05-23 | 2022-06-21 | Urthecast Corp. | Synthetic aperture radar imaging apparatus and methods |
CA3064586A1 (en) | 2017-05-23 | 2018-11-29 | King Abdullah City Of Science And Technology | Synthetic aperture radar imaging apparatus and methods for moving targets |
US11525910B2 (en) | 2017-11-22 | 2022-12-13 | Spacealpha Insights Corp. | Synthetic aperture radar apparatus and methods |
US10503843B2 (en) | 2017-12-19 | 2019-12-10 | Eagle View Technologies, Inc. | Supervised automatic roof modeling |
US11094113B2 (en) | 2019-12-04 | 2021-08-17 | Geomni, Inc. | Systems and methods for modeling structures using point clouds derived from stereoscopic image pairs |
US11915341B2 (en) * | 2022-02-14 | 2024-02-27 | Adobe Inc. | Repeat object blending |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9465987B1 (en) * | 2015-03-17 | 2016-10-11 | Exelis, Inc. | Monitoring and detecting weather conditions based on images acquired from image sensor aboard mobile platforms |
Family Cites Families (193)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US2273876A (en) * | 1940-02-12 | 1942-02-24 | Frederick W Lutz | Apparatus for indicating tilt of cameras |
US3153784A (en) | 1959-12-24 | 1964-10-20 | Us Industries Inc | Photo radar ground contour mapping system |
US5345086A (en) | 1962-11-28 | 1994-09-06 | Eaton Corporation | Automatic map compilation system |
US3621326A (en) | 1968-09-30 | 1971-11-16 | Itek Corp | Transformation system |
US3594556A (en) * | 1969-01-08 | 1971-07-20 | Us Navy | Optical sight with electronic image stabilization |
US3661061A (en) * | 1969-05-05 | 1972-05-09 | Atomic Energy Commission | Picture position finder |
US3614410A (en) | 1969-06-12 | 1971-10-19 | Knight V Bailey | Image rectifier |
US3716669A (en) * | 1971-05-14 | 1973-02-13 | Japan Eng Dev Co | Mapping rectifier for generating polarstereographic maps from satellite scan signals |
US3725563A (en) * | 1971-12-23 | 1973-04-03 | Singer Co | Method of perspective transformation in scanned raster visual display |
US3864513A (en) * | 1972-09-11 | 1975-02-04 | Grumman Aerospace Corp | Computerized polarimetric terrain mapping system |
US4015080A (en) * | 1973-04-30 | 1977-03-29 | Elliott Brothers (London) Limited | Display devices |
JPS5223975Y2 (en) * | 1973-05-29 | 1977-05-31 | ||
US3877799A (en) * | 1974-02-06 | 1975-04-15 | United Kingdom Government | Method of recording the first frame in a time index system |
DE2510044A1 (en) * | 1975-03-07 | 1976-09-16 | Siemens Ag | ARRANGEMENT FOR RECORDING CHARACTERS USING MOSAIC PENCILS |
US4707698A (en) | 1976-03-04 | 1987-11-17 | Constant James N | Coordinate measurement and radar device using image scanner |
US4240108A (en) | 1977-10-03 | 1980-12-16 | Grumman Aerospace Corporation | Vehicle controlled raster display system |
JPS5637416Y2 (en) * | 1977-10-14 | 1981-09-02 | ||
IT1095061B (en) * | 1978-05-19 | 1985-08-10 | Conte Raffaele | EQUIPMENT FOR MAGNETIC REGISTRATION OF CASUAL EVENTS RELATED TO MOBILE VEHICLES |
US4396942A (en) * | 1979-04-19 | 1983-08-02 | Jackson Gates | Video surveys |
FR2461305B1 (en) | 1979-07-06 | 1985-12-06 | Thomson Csf | MAP INDICATOR SYSTEM MORE PARTICULARLY FOR AIR NAVIGATION |
DE2939681A1 (en) * | 1979-09-29 | 1981-04-30 | Agfa-Gevaert Ag, 5090 Leverkusen | METHOD AND DEVICE FOR MONITORING THE QUALITY IN THE PRODUCTION OF PHOTOGRAPHIC IMAGES |
DE2940871C2 (en) * | 1979-10-09 | 1983-11-10 | Messerschmitt-Bölkow-Blohm GmbH, 8012 Ottobrunn | Photogrammetric method for aircraft and spacecraft for digital terrain display |
US4387056A (en) * | 1981-04-16 | 1983-06-07 | E. I. Du Pont De Nemours And Company | Process for separating zero-valent nickel species from divalent nickel species |
US4382678A (en) * | 1981-06-29 | 1983-05-10 | The United States Of America As Represented By The Secretary Of The Army | Measuring of feature for photo interpretation |
US4463380A (en) * | 1981-09-25 | 1984-07-31 | Vought Corporation | Image processing system |
US4495500A (en) * | 1982-01-26 | 1985-01-22 | Sri International | Topographic data gathering method |
US4490742A (en) | 1982-04-23 | 1984-12-25 | Vcs, Incorporated | Encoding apparatus for a closed circuit television system |
US4586138A (en) * | 1982-07-29 | 1986-04-29 | The United States Of America As Represented By The United States Department Of Energy | Route profile analysis system and method |
US4491399A (en) * | 1982-09-27 | 1985-01-01 | Coherent Communications, Inc. | Method and apparatus for recording a digital signal on motion picture film |
US4527055A (en) * | 1982-11-15 | 1985-07-02 | Honeywell Inc. | Apparatus for selectively viewing either of two scenes of interest |
FR2536851B1 (en) | 1982-11-30 | 1985-06-14 | Aerospatiale | RECOGNITION SYSTEM COMPRISING AN AIR VEHICLE TURNING AROUND ITS LONGITUDINAL AXIS |
US4489322A (en) | 1983-01-27 | 1984-12-18 | The United States Of America As Represented By The Secretary Of The Air Force | Radar calibration using direct measurement equipment and oblique photometry |
US4635136A (en) * | 1984-02-06 | 1987-01-06 | Rochester Institute Of Technology | Method and apparatus for storing a massive inventory of labeled images |
US4686474A (en) * | 1984-04-05 | 1987-08-11 | Deseret Research, Inc. | Survey system for collection and real time processing of geophysical data |
US4814711A (en) * | 1984-04-05 | 1989-03-21 | Deseret Research, Inc. | Survey system and method for real time collection and processing of geophysicals data using signals from a global positioning satellite network |
US4673988A (en) * | 1985-04-22 | 1987-06-16 | E.I. Du Pont De Nemours And Company | Electronic mosaic imaging process |
US4653136A (en) * | 1985-06-21 | 1987-03-31 | Denison James W | Wiper for rear view mirror |
EP0211623A3 (en) * | 1985-08-01 | 1988-09-21 | British Aerospace Public Limited Company | Identification of ground targets in airborne surveillance radar returns |
US4953227A (en) * | 1986-01-31 | 1990-08-28 | Canon Kabushiki Kaisha | Image mosaic-processing method and apparatus |
US4653316A (en) * | 1986-03-14 | 1987-03-31 | Kabushiki Kaisha Komatsu Seisakusho | Apparatus mounted on vehicles for detecting road surface conditions |
US4688092A (en) * | 1986-05-06 | 1987-08-18 | Ford Aerospace & Communications Corporation | Satellite camera image navigation |
US4956872A (en) | 1986-10-31 | 1990-09-11 | Canon Kabushiki Kaisha | Image processing apparatus capable of random mosaic and/or oil-painting-like processing |
JPS63202182A (en) * | 1987-02-18 | 1988-08-22 | Olympus Optical Co Ltd | Tilted dot pattern forming method |
US4814896A (en) * | 1987-03-06 | 1989-03-21 | Heitzman Edward F | Real time video data acquistion systems |
US5164825A (en) | 1987-03-30 | 1992-11-17 | Canon Kabushiki Kaisha | Image processing method and apparatus for mosaic or similar processing therefor |
US4807024A (en) * | 1987-06-08 | 1989-02-21 | The University Of South Carolina | Three-dimensional display methods and apparatus |
US4899296A (en) * | 1987-11-13 | 1990-02-06 | Khattak Anwar S | Pavement distress survey system |
US4843463A (en) * | 1988-05-23 | 1989-06-27 | Michetti Joseph A | Land vehicle mounted audio-visual trip recorder |
GB8826550D0 (en) * | 1988-11-14 | 1989-05-17 | Smiths Industries Plc | Image processing apparatus and methods |
US4906198A (en) * | 1988-12-12 | 1990-03-06 | International Business Machines Corporation | Circuit board assembly and contact pin for use therein |
JP2765022B2 (en) * | 1989-03-24 | 1998-06-11 | キヤノン販売株式会社 | 3D image forming device |
US5617224A (en) * | 1989-05-08 | 1997-04-01 | Canon Kabushiki Kaisha | Imae processing apparatus having mosaic processing feature that decreases image resolution without changing image size or the number of pixels |
US5086314A (en) * | 1990-05-21 | 1992-02-04 | Nikon Corporation | Exposure control apparatus for camera |
JPH0316377A (en) * | 1989-06-14 | 1991-01-24 | Kokusai Denshin Denwa Co Ltd <Kdd> | Method and apparatus for reducing binary picture |
US5166789A (en) | 1989-08-25 | 1992-11-24 | Space Island Products & Services, Inc. | Geographical surveying using cameras in combination with flight computers to obtain images with overlaid geographical coordinates |
JP3147358B2 (en) * | 1990-02-23 | 2001-03-19 | ミノルタ株式会社 | Camera that can record location data |
US5335072A (en) * | 1990-05-30 | 1994-08-02 | Minolta Camera Kabushiki Kaisha | Photographic system capable of storing information on photographed image data |
EP0464263A3 (en) * | 1990-06-27 | 1992-06-10 | Siemens Aktiengesellschaft | Device for obstacle detection for pilots of low flying aircrafts |
US5191174A (en) * | 1990-08-01 | 1993-03-02 | International Business Machines Corporation | High density circuit board and method of making same |
US5200793A (en) * | 1990-10-24 | 1993-04-06 | Kaman Aerospace Corporation | Range finding array camera |
US5155597A (en) | 1990-11-28 | 1992-10-13 | Recon/Optical, Inc. | Electro-optical imaging array with motion compensation |
US5337093A (en) * | 1990-12-19 | 1994-08-09 | Mitsubishi Denki Kabushiki Kaisha | Projection television system including a plurality of display elements with corresponding optical axes incident to a screen at different points offset from the screen center |
JPH04250436A (en) | 1991-01-11 | 1992-09-07 | Pioneer Electron Corp | Image pickup device |
US5265173A (en) | 1991-03-20 | 1993-11-23 | Hughes Aircraft Company | Rectilinear object image matcher |
US5369443A (en) | 1991-04-12 | 1994-11-29 | Abekas Video Systems, Inc. | Digital video effects generator |
CA2066280C (en) | 1991-04-16 | 1997-12-09 | Masaru Hiramatsu | Image pickup system with a image pickup device for control |
US5555018A (en) | 1991-04-25 | 1996-09-10 | Von Braun; Heiko S. | Large-scale mapping of parameters of multi-dimensional structures in natural environments |
US5231435A (en) * | 1991-07-12 | 1993-07-27 | Blakely Bruce W | Aerial camera mounting apparatus |
EP0530391B1 (en) * | 1991-09-05 | 1996-12-11 | Nec Corporation | Image pickup system capable of producing correct image signals of an object zone |
US5677515A (en) | 1991-10-18 | 1997-10-14 | Trw Inc. | Shielded multilayer printed wiring board, high frequency, high isolation |
US5402170A (en) * | 1991-12-11 | 1995-03-28 | Eastman Kodak Company | Hand-manipulated electronic camera tethered to a personal computer |
US5315691A (en) * | 1992-01-22 | 1994-05-24 | Brother Kogyo Kabushiki Kaisha | Print control apparatus |
US5247356A (en) | 1992-02-14 | 1993-09-21 | Ciampa John A | Method and apparatus for mapping and measuring land |
US5251037A (en) | 1992-02-18 | 1993-10-05 | Hughes Training, Inc. | Method and apparatus for generating high resolution CCD camera images |
US5270756A (en) | 1992-02-18 | 1993-12-14 | Hughes Training, Inc. | Method and apparatus for generating high resolution vidicon camera images |
US5363318A (en) * | 1992-03-23 | 1994-11-08 | Eastman Kodak Company | Method and apparatus for adaptive color characterization and calibration |
US5506644A (en) * | 1992-08-18 | 1996-04-09 | Olympus Optical Co., Ltd. | Camera |
US5481479A (en) * | 1992-12-10 | 1996-01-02 | Loral Fairchild Corp. | Nonlinear scanning to optimize sector scan electro-optic reconnaissance system performance |
US5342999A (en) | 1992-12-21 | 1994-08-30 | Motorola, Inc. | Apparatus for adapting semiconductor die pads and method therefor |
US5414462A (en) * | 1993-02-11 | 1995-05-09 | Veatch; John W. | Method and apparatus for generating a comprehensive survey map |
US5508736A (en) * | 1993-05-14 | 1996-04-16 | Cooper; Roger D. | Video signal processing apparatus for producing a composite signal for simultaneous display of data and video information |
US5467271A (en) | 1993-12-17 | 1995-11-14 | Trw, Inc. | Mapping and analysis system for precision farming applications |
KR970007807B1 (en) * | 1993-12-30 | 1997-05-16 | 엘지전자 주식회사 | White balance device |
DE69532126T2 (en) * | 1994-05-19 | 2004-07-22 | Geospan Corp., Plymouth | METHOD FOR COLLECTING AND PROCESSING VISUAL AND SPATIAL POSITION INFORMATION |
RU2153700C2 (en) * | 1995-04-17 | 2000-07-27 | Спейс Системз/Лорал, Инк. | Orientation and image shaping control system (design versions) |
US5604534A (en) * | 1995-05-24 | 1997-02-18 | Omni Solutions International, Ltd. | Direct digital airborne panoramic camera system and method |
US5668593A (en) | 1995-06-07 | 1997-09-16 | Recon/Optical, Inc. | Method and camera system for step frame reconnaissance with motion compensation |
US5963664A (en) | 1995-06-22 | 1999-10-05 | Sarnoff Corporation | Method and system for image combination using a parallax-based technique |
US5835133A (en) | 1996-01-23 | 1998-11-10 | Silicon Graphics, Inc. | Optical system for single camera stereo video |
US5894323A (en) * | 1996-03-22 | 1999-04-13 | Tasc, Inc, | Airborne imaging system using global positioning system (GPS) and inertial measurement unit (IMU) data |
JP3505115B2 (en) * | 1999-04-28 | 2004-03-08 | 富士通株式会社 | Image processing device and program recording medium |
US5844602A (en) | 1996-05-07 | 1998-12-01 | Recon/Optical, Inc. | Electro-optical imaging array and camera system with pitch rate image motion compensation which can be used in an airplane in a dive bomb maneuver |
US5798786A (en) * | 1996-05-07 | 1998-08-25 | Recon/Optical, Inc. | Electro-optical imaging detector array for a moving vehicle which includes two axis image motion compensation and transfers pixels in row directions and column directions |
US5841574A (en) | 1996-06-28 | 1998-11-24 | Recon/Optical, Inc. | Multi-special decentered catadioptric optical system |
US6108032A (en) | 1996-11-05 | 2000-08-22 | Lockheed Martin Fairchild Systems | System and method for image motion compensation of a CCD image sensor |
EP0937230B1 (en) * | 1996-11-05 | 2003-04-09 | BAE SYSTEMS Information and Electronic Systems Integration Inc. | Electro-optical reconnaissance system with forward motion compensation |
US6151410A (en) * | 1996-11-19 | 2000-11-21 | Seiko Epson Corporation | Image processing apparatus, image processing method and medium for storing image-processing control program |
RU2127075C1 (en) * | 1996-12-11 | 1999-03-10 | Корженевский Александр Владимирович | Method for producing tomographic image of body and electrical-impedance tomographic scanner |
JPH10186315A (en) * | 1996-12-27 | 1998-07-14 | Sharp Corp | Liquid crystal display device and driving method therefor |
US6249315B1 (en) * | 1997-03-24 | 2001-06-19 | Jack M. Holm | Strategy for pictorial digital image processing |
US6222583B1 (en) * | 1997-03-27 | 2001-04-24 | Nippon Telegraph And Telephone Corporation | Device and system for labeling sight images |
US6597818B2 (en) * | 1997-05-09 | 2003-07-22 | Sarnoff Corporation | Method and apparatus for performing geo-spatial registration of imagery |
JPH10319871A (en) * | 1997-05-19 | 1998-12-04 | Kouha:Kk | Led display device |
US6157747A (en) | 1997-08-01 | 2000-12-05 | Microsoft Corporation | 3-dimensional image rotation method and apparatus for producing image mosaics |
US6097854A (en) | 1997-08-01 | 2000-08-01 | Microsoft Corporation | Image mosaic construction system and apparatus with patch-based alignment, global block adjustment and pair-wise motion-based local warping |
AU9783798A (en) | 1997-10-06 | 1999-04-27 | John A. Ciampa | Digital-image mapping |
WO1999024936A1 (en) | 1997-11-10 | 1999-05-20 | Gentech Corporation | System and method for generating super-resolution-enhanced mosaic images |
US5852753A (en) | 1997-11-10 | 1998-12-22 | Lo; Allen Kwok Wah | Dual-lens camera with shutters for taking dual or single images |
US6037945A (en) | 1997-12-16 | 2000-03-14 | Xactware, Inc. | Graphical method for modeling and estimating construction costs |
US6094215A (en) * | 1998-01-06 | 2000-07-25 | Intel Corporation | Method of determining relative camera orientation position to create 3-D visual images |
US6791711B1 (en) * | 1998-06-24 | 2004-09-14 | Canon Kabushiki Kaisha | Image processing method, image processing apparatus, and recording medium |
US6130705A (en) | 1998-07-10 | 2000-10-10 | Recon/Optical, Inc. | Autonomous electro-optical framing camera system with constant ground resolution, unmanned airborne vehicle therefor, and methods of use |
JP4245699B2 (en) | 1998-09-16 | 2009-03-25 | オリンパス株式会社 | Imaging device |
US6434265B1 (en) | 1998-09-25 | 2002-08-13 | Apple Computers, Inc. | Aligning rectilinear images in 3D through projective registration and calibration |
DE19857667A1 (en) | 1998-12-15 | 2000-08-17 | Aerowest Photogrammetrie H Ben | Process for creating a three-dimensional object description |
US6167300A (en) | 1999-03-08 | 2000-12-26 | Tci Incorporated | Electric mammograph |
US6714243B1 (en) * | 1999-03-22 | 2004-03-30 | Biomorphic Vlsi, Inc. | Color filter pattern |
JP2000311243A (en) * | 1999-04-28 | 2000-11-07 | Sony Corp | Image color correction method and device |
DE19922341C2 (en) * | 1999-05-14 | 2002-08-29 | Zsp Geodaetische Sys Gmbh | Method and arrangement for determining the spatial coordinates of at least one object point |
AUPQ056099A0 (en) * | 1999-05-25 | 1999-06-17 | Silverbrook Research Pty Ltd | A method and apparatus (pprint01) |
JP5210473B2 (en) * | 1999-06-21 | 2013-06-12 | 株式会社半導体エネルギー研究所 | Display device |
US6639596B1 (en) | 1999-09-20 | 2003-10-28 | Microsoft Corporation | Stereo reconstruction from multiperspective panoramas |
US6650771B1 (en) * | 1999-11-22 | 2003-11-18 | Eastman Kodak Company | Color management system incorporating parameter control channels |
DE10022009C2 (en) * | 1999-11-26 | 2002-12-05 | Inb Vision Ag | Method and device for determining and at least partially correcting the errors of an image display system |
US6754279B2 (en) * | 1999-12-20 | 2004-06-22 | Texas Instruments Incorporated | Digital still camera system and method |
WO2001048683A1 (en) * | 1999-12-29 | 2001-07-05 | Geospan Corporation | Any aspect passive volumetric image processing method |
US6826539B2 (en) | 1999-12-31 | 2004-11-30 | Xactware, Inc. | Virtual structure data repository and directory |
US6829584B2 (en) | 1999-12-31 | 2004-12-07 | Xactware, Inc. | Virtual home data repository and directory |
US6810383B1 (en) | 2000-01-21 | 2004-10-26 | Xactware, Inc. | Automated task management and evaluation |
AU3047801A (en) * | 2000-02-03 | 2001-08-14 | Alst Technical Excellence Center | Image resolution improvement using a color mosaic sensor |
AU2001271238A1 (en) * | 2000-03-16 | 2001-09-24 | The Johns-Hopkins University | Light detection and ranging (lidar) mapping system |
IL151951A0 (en) * | 2000-03-29 | 2003-04-10 | Astrovision International Inc | Direct broadcast imaging satellite system, apparatus and method for providing real-time, continuous monitoring of earth from geostationary earth orbit and related services |
US6594388B1 (en) * | 2000-05-25 | 2003-07-15 | Eastman Kodak Company | Color image reproduction of scenes with preferential color mapping and scene-dependent tone scaling |
JP2002024815A (en) * | 2000-06-13 | 2002-01-25 | Internatl Business Mach Corp <Ibm> | Image conversion method for converting into enlarged image data, image processing device, and image display device |
US7184072B1 (en) | 2000-06-15 | 2007-02-27 | Power View Company, L.L.C. | Airborne inventory and inspection system and apparatus |
US6834128B1 (en) | 2000-06-16 | 2004-12-21 | Hewlett-Packard Development Company, L.P. | Image mosaicing system and method adapted to mass-market hand-held digital cameras |
US6484101B1 (en) * | 2000-08-16 | 2002-11-19 | Imagelinks, Inc. | 3-dimensional interactive image modeling system |
US7313289B2 (en) * | 2000-08-30 | 2007-12-25 | Ricoh Company, Ltd. | Image processing method and apparatus and computer-readable storage medium using improved distortion correction |
US6421610B1 (en) * | 2000-09-15 | 2002-07-16 | Ernest A. Carroll | Method of preparing and disseminating digitized geospatial data |
US6959120B1 (en) * | 2000-10-27 | 2005-10-25 | Microsoft Corporation | Rebinning methods and arrangements for use in compressing image-based rendering (IBR) data |
US7034862B1 (en) * | 2000-11-14 | 2006-04-25 | Eastman Kodak Company | System and method for processing electronically captured images to emulate film tonescale and color |
AU2002308651A1 (en) * | 2001-05-04 | 2002-11-18 | Leberl, Franz, W. | Digital camera for and method of obtaining overlapping images |
US7046401B2 (en) * | 2001-06-01 | 2006-05-16 | Hewlett-Packard Development Company, L.P. | Camera-based document scanning system using multiple-pass mosaicking |
GB2376342A (en) | 2001-06-08 | 2002-12-11 | Ever Ready Ltd | Optimised alkaline electrochemical cells |
US7509241B2 (en) * | 2001-07-06 | 2009-03-24 | Sarnoff Corporation | Method and apparatus for automatically generating a site model |
US20030043824A1 (en) * | 2001-08-31 | 2003-03-06 | Remboski Donald J. | Vehicle active network and device |
US6747686B1 (en) * | 2001-10-05 | 2004-06-08 | Recon/Optical, Inc. | High aspect stereoscopic mode camera and method |
US7262790B2 (en) | 2002-01-09 | 2007-08-28 | Charles Adams Bakewell | Mobile enforcement platform with aimable violation identification and documentation system for multiple traffic violation types across all lanes in moving traffic, generating composite display images and data to support citation generation, homeland security, and monitoring |
TW550521B (en) | 2002-02-07 | 2003-09-01 | Univ Nat Central | Method for re-building 3D model of house in a semi-automatic manner using edge segments of buildings |
US6894809B2 (en) | 2002-03-01 | 2005-05-17 | Orasee Corp. | Multiple angle display produced from remote optical sensing devices |
US7107285B2 (en) * | 2002-03-16 | 2006-09-12 | Questerra Corporation | Method, system, and program for an improved enterprise spatial system |
JP4184703B2 (en) | 2002-04-24 | 2008-11-19 | 大日本印刷株式会社 | Image correction method and system |
US7127348B2 (en) | 2002-09-20 | 2006-10-24 | M7 Visual Intelligence, Lp | Vehicle based data collection and processing system |
US7725258B2 (en) * | 2002-09-20 | 2010-05-25 | M7 Visual Intelligence, L.P. | Vehicle based data collection and processing system and imaging sensor system and methods thereof |
US6928194B2 (en) | 2002-09-19 | 2005-08-09 | M7 Visual Intelligence, Lp | System for mosaicing digital ortho-images |
JP4235426B2 (en) | 2002-09-20 | 2009-03-11 | キヤノン株式会社 | Imaging device |
US7424133B2 (en) | 2002-11-08 | 2008-09-09 | Pictometry International Corporation | Method and apparatus for capturing, geolocating and measuring oblique images |
EP1696204B1 (en) | 2002-11-08 | 2015-01-28 | Pictometry International Corp. | Method for capturing, geolocating and measuring oblique images |
US7018050B2 (en) * | 2003-09-08 | 2006-03-28 | Hewlett-Packard Development Company, L.P. | System and method for correcting luminance non-uniformity of obliquely projected images |
JP2005151536A (en) * | 2003-10-23 | 2005-06-09 | Nippon Dempa Kogyo Co Ltd | Crystal oscillator |
US7916940B2 (en) | 2004-01-31 | 2011-03-29 | Hewlett-Packard Development Company | Processing of mosaic digital images |
WO2005088251A1 (en) | 2004-02-27 | 2005-09-22 | Intergraph Software Technologies Company | Forming a single image from overlapping images |
US20060195858A1 (en) | 2004-04-15 | 2006-08-31 | Yusuke Takahashi | Video object recognition device and recognition method, video annotation giving device and giving method, and program |
US20060028550A1 (en) * | 2004-08-06 | 2006-02-09 | Palmer Robert G Jr | Surveillance system and method |
US7728833B2 (en) | 2004-08-18 | 2010-06-01 | Sarnoff Corporation | Method for generating a three-dimensional model of a roof structure |
US8078396B2 (en) | 2004-08-31 | 2011-12-13 | Meadow William D | Methods for and apparatus for generating a continuum of three dimensional image data |
EP1637838B1 (en) * | 2004-09-15 | 2009-09-23 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Processing of remote sensing data |
US7348895B2 (en) * | 2004-11-03 | 2008-03-25 | Lagassey Paul J | Advanced automobile accident detection, data recordation and reporting system |
US7142984B2 (en) | 2005-02-08 | 2006-11-28 | Harris Corporation | Method and apparatus for enhancing a digital elevation model (DEM) for topographical modeling |
JP4629096B2 (en) * | 2005-03-18 | 2011-02-09 | シャープ株式会社 | Image display device, image display monitor, and television receiver |
US7466244B2 (en) | 2005-04-21 | 2008-12-16 | Microsoft Corporation | Virtual earth rooftop overlay and bounding |
KR101171180B1 (en) * | 2005-07-15 | 2012-08-20 | 삼성전자주식회사 | Liquid crystal display |
US7554539B2 (en) * | 2005-07-27 | 2009-06-30 | Balfour Technologies Llc | System for viewing a collection of oblique imagery in a three or four dimensional virtual scene |
US7844499B2 (en) | 2005-12-23 | 2010-11-30 | Sharp Electronics Corporation | Integrated solar agent business model |
US7778491B2 (en) | 2006-04-10 | 2010-08-17 | Microsoft Corporation | Oblique image stitching |
JP4815267B2 (en) * | 2006-05-11 | 2011-11-16 | オリンパスイメージング株式会社 | White balance control method, imaging apparatus, and white balance control program |
EP2036043A2 (en) | 2006-06-26 | 2009-03-18 | Lockheed Martin Corporation | Method and system for providing a perspective view image by intelligent fusion of a plurality of sensor data |
US7873238B2 (en) * | 2006-08-30 | 2011-01-18 | Pictometry International Corporation | Mosaic oblique images and methods of making and using same |
DE102007030781A1 (en) | 2006-10-11 | 2008-04-17 | Gta Geoinformatik Gmbh | Method for texturing virtual three-dimensional objects |
IL179344A (en) * | 2006-11-16 | 2014-02-27 | Rafael Advanced Defense Sys | Method for tracking a moving platform |
US8027533B2 (en) * | 2007-03-19 | 2011-09-27 | Sti Medical Systems, Llc | Method of automated image color calibration |
US7832267B2 (en) | 2007-04-25 | 2010-11-16 | Ecometriks, Llc | Method for determining temporal solar irradiance values |
WO2009025928A2 (en) | 2007-06-19 | 2009-02-26 | Ch2M Hill, Inc. | Systems and methods for solar mapping, determining a usable area for solar energy production and/or providing solar information |
US7991226B2 (en) * | 2007-10-12 | 2011-08-02 | Pictometry International Corporation | System and process for color-balancing a series of oblique images |
US8417061B2 (en) | 2008-02-01 | 2013-04-09 | Sungevity Inc. | Methods and systems for provisioning energy systems |
US8275194B2 (en) | 2008-02-15 | 2012-09-25 | Microsoft Corporation | Site modeling using image data fusion |
CN101978395B (en) | 2008-04-23 | 2012-10-03 | 株式会社博思科 | Building roof outline recognizing device, and building roof outline recognizing method |
US8401222B2 (en) | 2009-05-22 | 2013-03-19 | Pictometry International Corp. | System and process for roof measurement using aerial imagery |
US8823726B2 (en) * | 2011-02-16 | 2014-09-02 | Apple Inc. | Color balance |
US8854370B2 (en) * | 2011-02-16 | 2014-10-07 | Apple Inc. | Color waveform |
US9183538B2 (en) | 2012-03-19 | 2015-11-10 | Pictometry International Corp. | Method and system for quick square roof reporting |
US9460521B2 (en) * | 2012-06-18 | 2016-10-04 | St-Ericsson Sa | Digital image analysis |
KR20160089572A (en) * | 2015-01-19 | 2016-07-28 | 삼성디스플레이 주식회사 | Liquid crystal display |
-
2007
- 2007-10-12 US US11/871,740 patent/US7991226B2/en not_active Expired - Fee Related
-
2008
- 2008-10-10 WO PCT/US2008/079509 patent/WO2009049151A1/en active Application Filing
- 2008-10-10 BR BRPI0818059-8A patent/BRPI0818059A2/en not_active IP Right Cessation
- 2008-10-10 EP EP08838178.5A patent/EP2210212A4/en not_active Ceased
- 2008-10-10 CA CA2702258A patent/CA2702258C/en active Active
- 2008-10-10 AU AU2008310726A patent/AU2008310726B2/en active Active
-
2011
- 2011-07-12 US US13/181,259 patent/US8649596B2/en active Active
-
2014
- 2014-01-13 US US14/153,772 patent/US8971624B2/en active Active
-
2015
- 2015-02-26 US US14/632,732 patent/US9503615B2/en active Active
-
2016
- 2016-11-21 US US15/357,490 patent/US20170236307A1/en not_active Abandoned
-
2018
- 2018-11-14 US US16/191,232 patent/US10580169B2/en active Active
-
2020
- 2020-03-02 US US16/806,347 patent/US11087506B2/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9465987B1 (en) * | 2015-03-17 | 2016-10-11 | Exelis, Inc. | Monitoring and detecting weather conditions based on images acquired from image sensor aboard mobile platforms |
Also Published As
Publication number | Publication date |
---|---|
US7991226B2 (en) | 2011-08-02 |
EP2210212A4 (en) | 2013-09-11 |
CA2702258A1 (en) | 2009-04-16 |
US8649596B2 (en) | 2014-02-11 |
US20140126816A1 (en) | 2014-05-08 |
US20200202583A1 (en) | 2020-06-25 |
BRPI0818059A2 (en) | 2015-03-31 |
WO2009049151A1 (en) | 2009-04-16 |
US8971624B2 (en) | 2015-03-03 |
US11087506B2 (en) | 2021-08-10 |
EP2210212A1 (en) | 2010-07-28 |
US20190102915A1 (en) | 2019-04-04 |
AU2008310726B2 (en) | 2013-02-07 |
US10580169B2 (en) | 2020-03-03 |
CA2702258C (en) | 2016-02-23 |
AU2008310726A1 (en) | 2009-04-16 |
US9503615B2 (en) | 2016-11-22 |
US20090097744A1 (en) | 2009-04-16 |
US20160248944A1 (en) | 2016-08-25 |
US20120183217A1 (en) | 2012-07-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11087506B2 (en) | System and process for color-balancing a series of oblique images | |
US10489953B2 (en) | Mosaic oblique images and methods of making and using same | |
US10424047B2 (en) | Cut line steering methods for forming a mosaic image of a geographical area | |
US5187754A (en) | Forming, with the aid of an overview image, a composite image from a mosaic of images | |
JP3796174B2 (en) | Imaging system, image processing apparatus, and camera | |
Doutre et al. | Fast vignetting correction and color matching for panoramic image stitching | |
US8750611B1 (en) | In-scene balancing of remotely sensed and aerial multiband imagery | |
Szeliski et al. | Image formation | |
Szeliski et al. | Image formation | |
WO2022034697A1 (en) | Astrophotography system, and recording medium | |
KR101762475B1 (en) | Image-processing-based digital pan·tilt·zoom·slide camera whereof the principal direction of vision of rectilinear image plane is periodically scanned along a predetermined trace |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: PICTOMETRY INTERNATIONAL CORP., NEW YORK Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:SCHULTZ, STEPHEN;GIUFFRIDA, FRANK D.;KUSAK, MATTHEW;SIGNING DATES FROM 20110126 TO 20110202;REEL/FRAME:045708/0714 |
|
AS | Assignment |
Owner name: HPS INVESTMENT PARTNERS, LLC,, NEW YORK Free format text: SECOND LIEN PATENT SECURITY AGREEMENT;ASSIGNOR:PICTOMETRY INTERNATIONAL CORP.;REEL/FRAME:046823/0755 Effective date: 20180814 |
|
AS | Assignment |
Owner name: MORGAN STANLEY SENIOR FUNDING, INC., AS COLLATERAL AGENT, NEW YORK Free format text: FIRST LIEN PATENT SECURITY AGREEMENT;ASSIGNOR:PICTOMETRY INTERNATIONAL CORP.;REEL/FRAME:046919/0065 Effective date: 20180814 Owner name: MORGAN STANLEY SENIOR FUNDING, INC., AS COLLATERAL Free format text: FIRST LIEN PATENT SECURITY AGREEMENT;ASSIGNOR:PICTOMETRY INTERNATIONAL CORP.;REEL/FRAME:046919/0065 Effective date: 20180814 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO PAY ISSUE FEE |