DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
All Information Disclosure Statements filed as of 04/24/2026 have been considered by examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Powell (WO 2021231129 A1) in view of Hung(TW 200913684 A).
With respect to claim 1, Powell teaches the method comprising: receiving an input image (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033) and a pointing angle (“tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34) associated with the input image (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033), wherein each input image in the stream of input images comprises a plurality of pixels (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033); interpolating an effective analytical projection, for each input image from a grid of predetermined analytical projections (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030), based on the respective pointing angle and plurality of pixels of each of the input images (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34), wherein the grid of predetermined analytical projections comprises a plurality of spaces that each correspond to respective predetermined pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030); generating a modified stream of input images, by mapping pixels of the input images to projected pixels of the modified images (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030), using the effective analytical projection (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030); and displaying the modified images (see figure 14).
Powell does not explicitly mention a stream of images.
Hung teaches a method for processing a stream of input images (“The video processor 1 24 performs processing on video streams (e.g., still images, moving video, and moving text) of video applications such as video cameras, video playback, and video conferencing.” Page 3 lines 19-21).
Hung is analogous art in the same field of endeavor as the claimed invention. Hung is directed towards an image distortion correction method (“In addition, the hue can also be offset. In the attempt to roll off the distortion, light shot lens roll-off correction is used.” Page 1 Description lines 7-8). A person of ordinary skill in the art would have found it obvious to utilize Hung’s teaching of applying image correction techniques to a stream of images such as during video conferencing, with Powell’s distortion correction methodology with the expectation that doing so would enable corrections to be done during video conferencing, an ability already mentioned in Powell itself (see in Powell “One disclosed example provides a videoconferencing system comprising a processor and a storage device storing instructions executable by the processor to obtain an image of a scene acquired via a camera” paragraph 0003).
With respect to claim 2, Powell and Hung teach the method of claim 1. Powell teaches wherein the interpolating comprises: plotting the pointing angle on the grid of predetermined analytical projections (“The controller 116 may include a distortion correction machine 206 configured to translate pixel locations of pixels of the raw image 204 according to a distortion correction projection 212 comprising a tilt parameter to generate the distortion corrected image 214. In other examples distortion correction may be performed on another computing device, such as a computer receiving image data from the camera 100 (e.g. a computing device into which camera 100 is integrated), rather than on controller 116. Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030); and interpolating the effective analytical projection from between at least two predetermined analytical projections that are nearest to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 3, Powell and Hung teach the method of claim 2. Powell further teaches wherein the interpolating comprises weighting each of the at least two predetermined analytical projections based on the proximity of their corresponding predetermined pointing angles to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 4, Powell and Hung teach the method of claim 3. Powell teaches wherein the at least two analytical projections are four predetermined analytical projections (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 6, Powell and Hung teach the method of claim 1. Powell further teaches wherein the predetermined analytical projections comprise a rectilinearly symmetric projection (“In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear)” paragraph 0031).
With respect to claim 7, Powell and Hung teach the method of claim 1, Powel further teaches wherein the stream of input images is received from an image sensor (“The tilted spherical projection maps image sensor coordinates in image sensor space to spherical coordinates.” Paragraph 0048), and wherein a lens associated with the image sensor has a horizontal field of view greater than 90 degrees (“In this example, the camera pitch angle is 12° as seen by the position of the horizontal H-Plane 1204 relative to the f-θ lens optical center. In this example the original raw image comprises a HFOV of 145.5° while the corrected cropped portion 1206 comprises a HFOV of 136” paragraph 0051).
Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable over Powell and Hung as applied to claim 1 above, and further in view of Zhang (DE 102013220013 A1).
With respect to claim 5, Powell and Hung teach the method of claim 1. Powell teaches wherein the pointing angle comprises a tilt angle (“The controller 116 may include a distortion correction machine 206 configured to translate pixel locations of pixels of the raw image 204 according to a distortion correction projection 212 comprising a tilt parameter to generate the distortion corrected image 214.” Paragraph 0030), but does not teach a pan angle.
Zhang teaches a pan angle (“The virtual tilt and / or tilt can be represented by a rotating matrix as follows: where α is the tilt angle and β is the tilt angle. It should be noted that the spin matrix as described herein is only one exemplary spin matrix and that other spin matrices could be used which contain other pose variations (changes in viewing angle) in addition to panning and / or tilt. For example, a rotation matrix may include 3 degrees of freedom changes, or it may include an entire position change.” Page 9 paragraph 6).
Zhang is analogous art in the same field of endeavor as the claimed invention. Zhang is directed at distortion correction (“The present invention proposes an efficient and effective image modeling and distortion correction process for ultra wide FOV cameras that utilizes a simple two-step approach and provides fast processing times and improved image quality without resorting to radial distortion correction” page 4 paragraph 3). A person of ordinary skill would have found it obvious to combine the teachings of Powell and Hung with Zhang, by utilizing Zhang’s teaching of distortion correction for wide FOV cameras including a pan angle in combination with Powell’s correction methodology, with the expectation that doing so would result is a system capable of correcting distortion of large FOV cameras and one that takes into account a pan angle allowing for further correction when an image sensor is panned (“In addition to dynamic view synthesis for ultra-wide FOV cameras, the proposed approach offers the functions of an effective environmental view and dynamic rear view mirrors with improved distortion correction.” Page 4 paragraph 5).
Claims 8-11,13-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Powell, Hung, and Sun (CN 114820797 A).
With respect to claim 8, Powell teaches the method comprising: receiving an input image (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033) and a pointing angle (“tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34) associated with the input image (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033); interpolating an effective analytical projection, for each input image from a plurality of predetermined analytical projections (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030), based on at least one or more pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34); generating a modified stream of input images, by mapping pixels of the input images to projected pixels of the modified images (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030), using the effective analytical projection (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030); and displaying the modified images (see figure 14).
Powell does not explicitly mention a stream of images or a correction process that uses an animation path.
Hung teaches a method for processing a stream of input images (“The video processor 1 24 performs processing on video streams (e.g., still images, moving video, and moving text) of video applications such as video cameras, video playback, and video conferencing.” Page 3 lines 19-21).
Hung is analogous art in the same field of endeavor as the claimed invention. Hung is directed towards an image distortion correction method (“In addition, the hue can also be offset. In the attempt to roll off the distortion, light shot lens roll-off correction is used.” Page 1 Description lines 7-8). A person of ordinary skill in the art would have found it obvious to utilize Hung’s teaching of applying image correction techniques to a stream of images such as during video conferencing, with Powell’s distortion correction methodology with the expectation that doing so would enable corrections to be done during video conferencing, an ability already mentioned in Powell itself (see in Powell “One disclosed example provides a videoconferencing system comprising a processor and a storage device storing instructions executable by the processor to obtain an image of a scene acquired via a camera” paragraph 0003).
Sun teaches an animation path associated with one or more pointing angles (“the characteristic point pi and pj is the projection of the P-respectively on the two frames, assuming that the camera moves from Oi to Oj, namely the motion between two frames and camera attitude information η is needed” page 6 paragraph 1 and “step 3-2) using constraint shooting adjacent frame number image and method of whole camera motion path, creating constraint local adjacent frame number and the whole underwater speed optimization camera motion equation” page 3 paragraph 7) and details wherein analytical projections associated with a respective location along the animation path that corresponds to a respective pointing angle of the one or more pointing angles (“step 3) based on constraint camera motion and image distortion correction of the back end optimization; step 3-1) establishing an error cost function and an increment equation and solving; step 3-2) using constraint shooting adjacent frame number image and method of whole camera motion path, creating constraint local adjacent frame number and the whole underwater speed optimization camera motion equation; step 3-3) when creating SLAM word bag, through fusion radial distortion, tangential distortion and scattering coefficient of the method, the shot image and the characteristic point for distortion correction;” page 3 paragraphs 5-7).
Sun is analogous art in the same field of endeavor as the claimed invention. Sun is directed towards image distortion correction (“The object of the present invention is achieved in the following way: An underwater vision SLAM method based on constraint camera motion and distortion correction” page 3 paragraph 2). A person of ordinary skill in the art would find it obvious to combine the teachings of Powell and Hung with Sun, by utilizing Sun’s teaching of distortion correction by tracking camera motion inside of Powell’s distortion correction method that already utilized positional camera properties in the form of tilt angles, with the expectation that doing so would lead to improved image quality in situations when the camera is in motion (“The purpose of the invention is to overcome the defect of the existing technology, providing underwater vision SLAM method based on constraint camera motion and distortion correction, optimizing camera motion equation by creating constraint local adjacent frame number and the whole underwater speed, performing local and global optimization to the underwater camera, taking the optimization equation as the auxiliary condition, solving the influence of the underwater environment, which can effectively improve the quality of the single camera visual real-time scene constructed in the underwater environment.” Page 3 paragraph 1)
With respect to claim 9, Powell, Hung, and Sun teach the method of claim 8. Powell further teaches wherein the interpolating comprises: plotting a pointing angle, from the one or more pointing angles, wherein the pointing angle corresponds to a respective input image (“The controller 116 may include a distortion correction machine 206 configured to translate pixel locations of pixels of the raw image 204 according to a distortion correction projection 212 comprising a tilt parameter to generate the distortion corrected image 214. In other examples distortion correction may be performed on another computing device, such as a computer receiving image data from the camera 100 (e.g. a computing device into which camera 100 is integrated), rather than on controller 116. Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030); and interpolating the effective analytical projection from between at least two predetermined analytical projections that are nearest to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
Powell does not teach an animation path or a stream of images.
Hung teaches a method for processing a stream of input images (“The video processor 1 24 performs processing on video streams (e.g., still images, moving video, and moving text) of video applications such as video cameras, video playback, and video conferencing.” Page 3 lines 19-21).
Sun teaches plotting angles along an animation map (“step 3-2) specifically comprises: The invention claims a method for limiting camera partial whole movement, recording as Ck, k + 1, k + 2, k + 3, k + 4 is a camera position of a certain frame number, Ck, k + 1, Ck + 1, k + 2, Ck + 2, k + 3 is the displacement path of the camera between the adjacent frame number, T is each frame interval time, by the speed optimization formula to limit local and whole rate is: wherein k is a certain position point in the camera movement process, T is the camera shooting time of adjacent frame number picture, n is the total shooting image total number -1.… the step 3-3) specifically comprises: solving the radial distortion and tangential distortion of the camera, so as to solve the distortion of the space coordinate and the pixel point coordinate of the characteristic point caused by the underwater environment; coordinate point Q [X, Y, Z] of the camera by four distortion correction coefficient h1, h2, l1, l2 and scattering coefficient k v, obtaining the correct pixel coordinate [xcd, ycd], namely by radial distortion and correction of tangential distortion: wherein the whole distortion correction coordinate is [xcd, ycd], the distortion coordinate is [x, y], h1 and h2 is the radial distortion coefficient of the central region and the edge region, l1 and l2 is the tangential distortion coefficient in x and y-axis direction, b2 the numerical value of the correction position of the transverse and longitudinal coordinate square sum, k is the scattering coefficient, dx, dy is the respectively of x, y axis direction”)
With respect to claim 10, Powell, Hung, and Sun teach the method of claim 9. Powell further teaches wherein which at least two predetermined analytical projections of the plurality of predetermined analytical projects are the nearest to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030) is measured via angular distance (“Further, different projections may be determined and/or stored for different tilt angles, thereby allowing an appropriate correction to be applied based upon a current camera tilt angle.” Paragraph 0023) between each predetermined analytical projection of the plurality of predetermined analytical projections and the plotted pointing angle (“Further, different projections may be determined and/or stored for different tilt angles, thereby allowing an appropriate correction to be applied based upon a current camera tilt angle.” Paragraph 0023 and “Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 11, Powell, Hung, and Sun teach the method of claim 9. Powell further teaches wherein the interpolating comprises weighting each of the at least two predetermined analytical projections based on the proximity of their corresponding pointing angles to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 13, Powell, Hung, and Sun teach the method of claim 8. Powell further teaches wherein the plurality of predetermined analytical projections comprise a rectilinearly symmetric projection (“In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear)” paragraph 0031).
With respect to claim 14, Powell teaches the method comprising: receiving an input image (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033) and a pointing angle (“tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34) associated with the input image (“The distortion correction projection 212 defines a relationship between the pixel locations of the raw image 204 and the translated pixel locations of the distortion corrected image 214 as an inverse function in which the sensor coordinates are mapped to projection plane and/or surface coordinates of the distortion correction projection 212.” Page 9 paragraph 0033); interpolating a first effective analytical projection, for each input image from a first plurality of predetermined analytical projections (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030), based on at least one or more pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34); generating a first modified stream of input images, by mapping pixels of the input images to projected pixels of the first modified images (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030), using the first effective analytical projection (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030). Powell additionally teaches interpolating a second effective analytical projection, for at least one input image, from a second plurality of predetermined analytical projections (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031), based on at least one of the one or more second pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031), wherein each predetermined analytical projection of the second plurality of predetermined analytical projections corresponds to a respective pointing angle of the one or more second pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031); generating a second modified input images, by mapping pixels of the input image to projected pixels of the second modified image (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031), using the second effective analytical projection (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031); and displaying the first and second modified streams of images (see figure 14).
Powell does not explicitly mention a stream of images or a correction process that uses an animation path.
Hung teaches a method for processing a stream of input images (“The video processor 1 24 performs processing on video streams (e.g., still images, moving video, and moving text) of video applications such as video cameras, video playback, and video conferencing.” Page 3 lines 19-21).
Hung is analogous art in the same field of endeavor as the claimed invention. Hung is directed towards an image distortion correction method (“In addition, the hue can also be offset. In the attempt to roll off the distortion, light shot lens roll-off correction is used.” Page 1 Description lines 7-8). A person of ordinary skill in the art would have found it obvious to utilize Hung’s teaching of applying image correction techniques to a stream of images such as during video conferencing, with Powell’s distortion correction methodology with the expectation that doing so would enable corrections to be done during video conferencing, an ability already mentioned in Powell itself (see in Powell “One disclosed example provides a videoconferencing system comprising a processor and a storage device storing instructions executable by the processor to obtain an image of a scene acquired via a camera” paragraph 0003).
Sun teaches a first animation path associated with one or more first pointing angles (“the characteristic point pi and pj is the projection of the P-respectively on the two frames, assuming that the camera moves from Oi to Oj, namely the motion between two frames and camera attitude information η is needed” page 6 paragraph 1 and “step 3-2) using constraint shooting adjacent frame number image and method of whole camera motion path, creating constraint local adjacent frame number and the whole underwater speed optimization camera motion equation” page 3 paragraph 7) and details wherein analytical projections associated with a respective location along the animation path that corresponds to a respective pointing angle of the one or more pointing angles (“step 3) based on constraint camera motion and image distortion correction of the back end optimization; step 3-1) establishing an error cost function and an increment equation and solving; step 3-2) using constraint shooting adjacent frame number image and method of whole camera motion path, creating constraint local adjacent frame number and the whole underwater speed optimization camera motion equation; step 3-3) when creating SLAM word bag, through fusion radial distortion, tangential distortion and scattering coefficient of the method, the shot image and the characteristic point for distortion correction;” page 3 paragraphs 5-7). Sun additionally teaches receiving a second animation path associated with one or more second pointing angles (“In order to further optimize the camera motion, the step 3-1) specifically comprises: using the relative relation between the position information of the two cameras and the relative motion information” page 3 paragraph 11).
Sun is analogous art in the same field of endeavor as the claimed invention. Sun is directed towards image distortion correction (“The object of the present invention is achieved in the following way: An underwater vision SLAM method based on constraint camera motion and distortion correction” page 3 paragraph 2). A person of ordinary skill in the art would find it obvious to combine the teachings of Powell and Hung with Sun, by utilizing Sun’s teaching of distortion correction by tracking camera motion inside of Powell’s distortion correction method that already utilized positional camera properties in the form of tilt angles, with the expectation that doing so would lead to improved image quality in situations when the camera is in motion (“The purpose of the invention is to overcome the defect of the existing technology, providing underwater vision SLAM method based on constraint camera motion and distortion correction, optimizing camera motion equation by creating constraint local adjacent frame number and the whole underwater speed, performing local and global optimization to the underwater camera, taking the optimization equation as the auxiliary condition, solving the influence of the underwater environment, which can effectively improve the quality of the single camera visual real-time scene constructed in the underwater environment.” Page 3 paragraph 1)
With respect to claim 15, Powell, Hung, and Sun teach the method of claim 14. Powell further teaches wherein at least one pointing angle of the plurality of second pointing angles is the same as at least one pointing angle of the one or more first pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031), and wherein at least one pointing angle of the plurality of second pointing angles is different than at least one pointing angle of the one or more first pointing angles (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030 and “tilt angle data from a tilt sensor 213 may be used to select a distortion correction projection 212 to apply, such that the tilt angle of the projection corresponds to a tilt angle of the camera as determined from the tilt sensor data. The tilt sensor 213 may be incorporated into the camera or into a computing device comprising the camera,” page 8 lines 30-34 and “In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear).” Paragraph 0031).
Sun additionally teaches wherein the second animation path comprises a plurality of second pointing angles (“after the map construction is started, based on the improved back end optimization of the bitmap method, not only using the pose information between the adjacent frame number and image conversion of the adjacent frame number, solving the target function to optimize the camera motion” page 7 paragraph 3 and “In order to further optimize the camera motion, the step 3-1) specifically comprises: using the relative relation between the position information of the two cameras and the relative motion information” page 3 paragraph 11), wherein the first animation comprises a plurality of first pointing angles (“after the map construction is started, based on the improved back end optimization of the bitmap method, not only using the pose information between the adjacent frame number and image conversion of the adjacent frame number, solving the target function to optimize the camera motion” page 7 paragraph 3 and “the characteristic point pi and pj is the projection of the P-respectively on the two frames, assuming that the camera moves from Oi to Oj, namely the motion between two frames and camera attitude information η is needed” page 6 paragraph 1 and “step 3-2) using constraint shooting adjacent frame number image and method of whole camera motion path, creating constraint local adjacent frame number and the whole underwater speed optimization camera motion equation” page 3 paragraph 7),
With respect to claim 16, Powell, Hung, and Sun teach the method of claim 14. Powell further teaches wherein the interpolating a first effective analytical projection comprises: plotting a pointing angle, from the one or more first pointing angles, along the first animation path, wherein the pointing angle corresponds to a respective input image (“The controller 116 may include a distortion correction machine 206 configured to translate pixel locations of pixels of the raw image 204 according to a distortion correction projection 212 comprising a tilt parameter to generate the distortion corrected image 214. In other examples distortion correction may be performed on another computing device, such as a computer receiving image data from the camera 100 (e.g. a computing device into which camera 100 is integrated), rather than on controller 116. Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection.” Paragraph 0030); and interpolating the first effective analytical projection from between at least two predetermined analytical projections of the first plurality of predetermined analytical projections that are nearest to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
Powell does not teach an animation path or a stream of images.
Hung teaches a method for processing a stream of input images (“The video processor 1 24 performs processing on video streams (e.g., still images, moving video, and moving text) of video applications such as video cameras, video playback, and video conferencing.” Page 3 lines 19-21).
Sun teaches plotting angles along an animation map (“step 3-2) specifically comprises: The invention claims a method for limiting camera partial whole movement, recording as Ck, k + 1, k + 2, k + 3, k + 4 is a camera position of a certain frame number, Ck, k + 1, Ck + 1, k + 2, Ck + 2, k + 3 is the displacement path of the camera between the adjacent frame number, T is each frame interval time, by the speed optimization formula to limit local and whole rate is: wherein k is a certain position point in the camera movement process, T is the camera shooting time of adjacent frame number picture, n is the total shooting image total number -1.… the step 3-3) specifically comprises: solving the radial distortion and tangential distortion of the camera, so as to solve the distortion of the space coordinate and the pixel point coordinate of the characteristic point caused by the underwater environment; coordinate point Q [X, Y, Z] of the camera by four distortion correction coefficient h1, h2, l1, l2 and scattering coefficient k v, obtaining the correct pixel coordinate [xcd, ycd], namely by radial distortion and correction of tangential distortion: wherein the whole distortion correction coordinate is [xcd, ycd], the distortion coordinate is [x, y], h1 and h2 is the radial distortion coefficient of the central region and the edge region, l1 and l2 is the tangential distortion coefficient in x and y-axis direction, b2 the numerical value of the correction position of the transverse and longitudinal coordinate square sum, k is the scattering coefficient, dx, dy is the respectively of x, y axis direction”).
With respect to claim 17, Powell, Hung, and Sun teach the method of claim 16. Powell further teaches wherein which at least two of the first plurality of predetermined analytical projections of the plurality of predetermined analytical projects are the nearest to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030) is measured via angular distance (“Further, different projections may be determined and/or stored for different tilt angles, thereby allowing an appropriate correction to be applied based upon a current camera tilt angle.” Paragraph 0023) between each predetermined analytical projection and the first the plurality of predetermined analytical projections and the plotted pointing angle (“Further, different projections may be determined and/or stored for different tilt angles, thereby allowing an appropriate correction to be applied based upon a current camera tilt angle.” Paragraph 0023 and “Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 18, Powell, Hung, and Sun teach the method of claim 16. Powell further teaches wherein the interpolating comprises weighting each of the at least two predetermined analytical projections based on the proximity of their corresponding pointing angles to the plotted pointing angle (“Note that the pixel locations of different pixels in the raw image may be translated and/or interpolated, such as by application of a mesh grid (e.g. the mesh file referred to above) indicating mapping of each integer (x, y) pixel of a distortion corrected image to a floating- point position within the original input image (x’, y’), on an individual pixel basis based on the distortion correction projection. As such, in different instances, pixel locations of different pixels may be translated differently (e.g., different direction and/or distance of translation for different pixels), pixel locations of different pixels may be translated the same (e.g., same direction and/or distance of translation for different pixels), and/or pixel locations of some pixels may remain the same between the raw image 204 and the distortion corrected image 214.” Paragraph 0030).
With respect to claim 20, Powell, Hung, and Sun teach the method of claim 14. Powell further teaches wherein the second plurality of predetermined analytical projections comprise a rectilinearly symmetric projection (“In some examples, a plurality of distortion correction projections 212 may be stored, e.g., corresponding to different tilt angles and/or different types of projections (e.g. cylindrical, spherical, and/or rectilinear)” paragraph 0031).
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Powell, Hung, and Sun as applied to claims 8 and 14 above, and further in view of Zhang.
With respect to claim 12, Powell, Hung, and Sun teach the method of claim 8. Powell teaches wherein the pointing angle comprises a tilt angle (“The controller 116 may include a distortion correction machine 206 configured to translate pixel locations of pixels of the raw image 204 according to a distortion correction projection 212 comprising a tilt parameter to generate the distortion corrected image 214.” Paragraph 0030), but does not teach a pan angle.
Zhang teaches a pan angle (“The virtual tilt and / or tilt can be represented by a rotating matrix as follows: where α is the tilt angle and β is the tilt angle. It should be noted that the spin matrix as described herein is only one exemplary spin matrix and that other spin matrices could be used which contain other pose variations (changes in viewing angle) in addition to panning and / or tilt. For example, a rotation matrix may include 3 degrees of freedom changes, or it may include an entire position change.” Page 9 paragraph 6).
Zhang is analogous art in the same field of endeavor as the claimed invention. Zhang is directed at distortion correction (“The present invention proposes an efficient and effective image modeling and distortion correction process for ultra wide FOV cameras that utilizes a simple two-step approach and provides fast processing times and improved image quality without resorting to radial distortion correction” page 4 paragraph 3). A person of ordinary skill would have found it obvious to combine the teachings of Powell, Hung and Sun with Zhang, by utilizing Zhang’s teaching of distortion correction for wide FOV cameras including a pan angle in combination with Powell’s correction methodology, with the expectation that doing so would result is a system capable of correcting distortion of large FOV cameras and one that takes into account a pan angle allowing for further correction when an image sensor is panned (“In addition to dynamic view synthesis for ultra-wide FOV cameras, the proposed approach offers the functions of an effective environmental view and dynamic rear view mirrors with improved distortion correction.” Page 4 paragraph 5).
With respect to claim 19, The method of claim 14, wherein the one or more first pointing angles and the one or more second pointing angles each comprises a respective tilt angle and pan angle (“The controller 116 may include a distortion correction machine 206 configured to translate pixel locations of pixels of the raw image 204 according to a distortion correction projection 212 comprising a tilt parameter to generate the distortion corrected image 214.” Paragraph 0030), but does not teach a pan angle.
Zhang teaches a pan angle (“The virtual tilt and / or tilt can be represented by a rotating matrix as follows: where α is the tilt angle and β is the tilt angle. It should be noted that the spin matrix as described herein is only one exemplary spin matrix and that other spin matrices could be used which contain other pose variations (changes in viewing angle) in addition to panning and / or tilt. For example, a rotation matrix may include 3 degrees of freedom changes, or it may include an entire position change.” Page 9 paragraph 6).
Zhang is analogous art in the same field of endeavor as the claimed invention. Zhang is directed at distortion correction (“The present invention proposes an efficient and effective image modeling and distortion correction process for ultra wide FOV cameras that utilizes a simple two-step approach and provides fast processing times and improved image quality without resorting to radial distortion correction” page 4 paragraph 3). A person of ordinary skill would have found it obvious to combine the teachings of Powell, Hung and Sun with Zhang, by utilizing Zhang’s teaching of distortion correction for wide FOV cameras including a pan angle in combination with Powell’s correction methodology, with the expectation that doing so would result is a system capable of correcting distortion of large FOV cameras and one that takes into account a pan angle allowing for further correction when an image sensor is panned (“In addition to dynamic view synthesis for ultra-wide FOV cameras, the proposed approach offers the functions of an effective environmental view and dynamic rear view mirrors with improved distortion correction.” Page 4 paragraph 5).
Response to Arguments
Applicant’s argument filed 01/27/2026 have been fully considered.
On page 8 applicant argues that Powell fails to teach “effective analytical projection that is interpolated from a grid of predetermined analytical projections that comprise a plurality of spaces that each correspond to respective predetermined pointing angles” (see page 8 paragraph 1 of remarks). The applicant specifically points out that mere pixel translations does not disclose effective analytical projection. The examiner disagrees with the applicant’s argument and notes that Powell is not directed to mere pixel translation but instead to distortion correction projection (See paragraph 30) and more broadly the application of a projection comprising a tilt parameter based on a camera pitch angle (See paragraph 0023). According the examiner maintains the claim 1 rejection of Powell in view of Hung. Subsequently the examiner also disagrees with the Applicant’s additional arguments revolving around the substantially similar claims 8 and 14, again maintaining their rejections. Thusly applicant’s arguments involving the allowability of the dependent claims and the use of additional prior art to remedy Powell’s deficiencies are considered moot.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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