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 .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 5-8 and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0084225 A1 to Kang in view of US 2012/0082368 A1 to Harai et al., hereinafter, “Harai”.
Claim 1. Kang teaches A method for detecting and correcting flying pixel artifacts in an image, the method comprises, [0011] a method for removing a flying pixel
receiving a depth image from an image sensor of a Time-of-flight camera system, acquiring a depth map to be processed [0005] acquiring a depth map to be processed, [0038] FIG. 1 a ToF sensor
wherein the depth image includes one or more flying pixels; [0005] …each pixel in the depth map…determining a candidate flying pixel and a target pixel according to the confidence level gradients
acquiring a depth value for each pixel in the received depth image; [0019] acquiring a confidence level of a depth value of each candidate reference pixel in all of the candidate reference pixels;
dynamically calculating a threshold value for each pixel based on the acquired depth value for each pixel; [0018] wherein each pixel in the depth map has a corresponding depth value and a confidence level of the depth value; … and generating a flying pixel depth threshold according to a depth value of the target pixel, [0022]
identifying a target pixel as a flying pixel based on the depth values of neighboring pixels, [0018] determining a candidate flying pixel and a target pixel according to the confidence level gradients, and generating a flying pixel depth threshold according to a depth value of the target pixel…acquiring depth values of a pair of adjacent pixels relative to the candidate flying pixel in the reference direction; acquiring a depth difference value between depth values of the candidate flying pixel and of each adjacent pixel in the pair of adjacent pixels, and determining whether the candidate flying pixel is a flying pixel according to the depth difference values and the flying pixel depth threshold
wherein identifying the target pixel as the flying pixel comprises: [0063] the candidate flying pixel is the target flying pixel.
obtaining the depth values of the neighboring pixels surrounding the target pixel; [0005] determining a candidate flying pixel and a target pixel according to the confidence level gradients, and generating a flying pixel depth threshold according to a depth value of the target pixel, wherein the target pixel is a pixel point being distanced from the candidate flying pixel within a preset range; determining a reference direction according to the gradient angle of the candidate flying pixel, and acquiring depth values of a pair of adjacent pixels relative to the candidate flying pixel in the reference direction
comparing the obtained depth values of the neighboring pixels with the depth value of the target pixel to estimate a depth deviation value; [0058] the deviation of the depth value is large, and thus it necessarily has a large deviation from depth values of surrounding pixels. In this embodiment, a depth difference value between depth values of the candidate flying pixel and of each adjacent pixel in the pair of adjacent pixels is acquired…
comparing the estimated depth deviation value with a dynamically determined threshold value of the target pixel; [0058] If change relative to the depth values of the adjacent pixels in the reference direction is greater than the flying pixel depth threshold, the candidate flying pixel is considered as being a flying pixel.
and identifying the target pixel as the flying pixel, in an event the depth deviation value exceeds the dynamically determined threshold value of the target pixel; [0058] If change relative to the depth values of the adjacent pixels in the reference direction is greater than the flying pixel depth threshold, the candidate flying pixel is considered as being a flying pixel.
Kang fails to explicitly teach modifying the depth value of the identified target pixel with an appropriate depth value to correct the flying pixel artifacts. Harai in the field of depth correction in image data, teaches and modifying the depth value of the identified target pixel with an appropriate depth value to correct the flying pixel artifacts, [0027] to apply correction which replaces a depth value of the correction target pixel by a representative depth value of the corresponding class.
wherein the modified depth value is estimated based on at least one of a mean pair of depth values from neighboring pixels or an optimum median depth value of neighboring pixels. [0081] The depth corrector 204 determines a corresponding class of a correction target pixel in a correction target block based on a pixel value of the correction target pixel, the pixel value statistics 24 of the respective classes, and pixel values of surrounding pixels of the correction target pixel. The depth corrector 204 replaces a depth value of the correction target pixel by a representative depth value 15 of the corresponding class. Understood to be best neighbor pair mean correction wherein, the corresponding class is the neighbor-pair.
[0044] The provisional clusterer 101 calculates an average value or median value of depth values of a plurality of pixels in the calculation block. [0102]
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kang with the teachings of Harai [0003-0005] to eliminate errors due to generating 3D content. Rationale applies to claims 2, 5-8, 11-13.
Claim 2. Kang further teaches The method as claimed in claim 1, wherein the received depth image with flying pixels comprises either a two-Dimensional (2D) depth image or a three-Dimensional (3D) depth image. [0004] screening flying pixels in a current photographed image (understood to be a 2D image)
Claim 5. Harai further teaches The method as claimed in claim 1, wherein the appropriate depth value is estimated using a best neighbor-pair mean correction method or a median reconstruction method. [0081] The depth corrector 204 determines a corresponding class of a correction target pixel in a correction target block based on a pixel value of the correction target pixel, the pixel value statistics 24 of the respective classes, and pixel values of surrounding pixels of the correction target pixel. The depth corrector 204 replaces a depth value of the correction target pixel by a representative depth value 15 of the corresponding class. Understood to be best neighbor pair mean correction wherein, the corresponding class is the neighbor-pair.
[0044] The provisional clusterer 101 calculates an average value or median value of depth values of a plurality of pixels in the calculation block. [0102]
Claim 6. Kang further teaches The method as claimed in claim 1, wherein the threshold value of each pixel is dynamically determined based on the depth value of the target pixel. [0005] wherein each pixel in the depth map…and generating a flying pixel depth threshold according to a depth value of the target pixel
Claim 7. Reviewed and analyzed in the same way as claim 1. See the above analysis and rationale.
Claim 8. Reviewed and analyzed in the same way as claim 2. See the above analysis and rationale.
Claim 11. Reviewed and analyzed in the same way as claim 5. See the above analysis and rationale.
Claim 12. Reviewed and analyzed in the same way as claim 6. See the above analysis and rationale.
Claim 13. Kang teaches A computer program product comprising a non-transitory computer readable medium, having thereon a computer program comprising program instructions, the computer program is loadable into a data processing unit and configured to cause execution of the method according to claim 1 when the computer program is run by the data processing unit. [0007]
Claim(s) 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0084225 A1 to Kang in view of US 2012/0082368 A1 to Harai et al., hereinafter, “Harai” and in further view of US 2018/0103243 A1 to Lee et al., hereinafter, “Lee”.
Claim 3. Kang fails to explicitly teach obtaining the depth values of the neighboring pixels from a 2D depth image comprises using a 2D image kernel to select neighboring depth information. Lee in the field of generating depth information (value) in image data, teaches wherein obtaining the depth values of the neighboring pixels from a 2D depth image comprises using a 2D image kernel to select neighboring depth information. [0097] which may be used as a depth map refinement unit. The processing module 204 may operate as a range filter which analyzes a size of N×N sliding window in the 2D image 206 to determine the kernel weights for use in refining the value of depth map (understood to be obtaining the depth values). In the range difference analysis step 302, the weights of range filter may depend on the range difference from nearby pixels (neighboring pixels) in the 2D image 206.
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kang with the teachings of Lee [0001], [0062] to improve the quality of 3D video contents.
Claim 9. Reviewed and analyzed in the same way as claim 3. See the above analysis and rationale.
Claim(s) 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0084225 A1 to Kang in view of US 2012/0082368 A1 to Harai et al., hereinafter, “Harai” and in further view of US 2020/0242779 A1 to Deng et al., hereinafter, “Deng”.
Claim 4. Kang fails to explicitly teach obtaining the depth values of the neighboring pixels for a 3D depth image comprises using a K-Dimensional Tree (KDTree) to select the neighboring pixels based on their spatial proximity. Deng in the field of depth correction in image data, teaches wherein obtaining the depth values of the neighboring pixels for a 3D depth image comprises using a K-Dimensional Tree (KDTree) to select the neighboring pixels based on their spatial proximity. [0023] A three-dimensional (3D) point cloud may be extracted from a depth image. For each point in the 3D point cloud, a local neighborhood is extracted via searching a k-dimensional (k-d) tree of the point cloud. A k-d tree is a data structure that organizes points in a space, where the points correspond to leaf nodes and non-leaf nodes of the tree represent divisions of the space. The local neighborhood may be extracted by searching the k-d tree for nearest neighbors
Thus, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Kang with the teachings of Deng [0003] to improve sensor data.
Claim 10. Reviewed and analyzed in the same way as claim 4. See the above analysis and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2020/0020133 A1 to Najaf-Zadeh et al.
Najaf-Zadeh [claim 13] smoothing modifies the query point (understood to be the equivalent of the identified target pixel)…identify neighboring points of the query point, wherein the query point is within one of the set of patches… modify the average depth value of the query point with the average depth value of the neighboring points. (understood to be the modified depth value of the target point)
Najaf-Zadeh [claim 13] identify neighboring points of the query point, wherein the query point (understood to be the equivalent of the target pixel) is within one of the set of patches, project the neighboring points on a projection plane of the query point, derive an average depth value of the neighboring points (understood to be a mean pair of depth values from neighboring pixel), determine whether a difference between the average depth value of the neighboring points and an average depth value of the query point is larger than a threshold, and when the difference is larger than the threshold, modify the average depth value of the query point (understood to be the modified depth value of the target point) with the average depth value of the neighboring points.
US 2020/0388044 A1 to Cen et al.
Cen [0051] search for the boundary points based on a KD tree and normal vector method
Cen [0099] (1) Before photographing, calibrate a TOF camera to ensure that depth information of point cloud data and RGB image mapping obtained by the TOF camera are accurate.
Cen [0109] 2) Specify a quantity of point cloud searches in the neighborhood, search for neighboring points of each point and a normal vector of each neighboring point through the KD tree
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DELOMIA L GILLIARD whose telephone number is (571)272-1681. The examiner can normally be reached 8am-5pm.
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/DELOMIA L GILLIARD/Primary Examiner, Art Unit 2661
