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 .
Response to Arguments
Applicant’s arguments (see remarks), filed 08/12/2025, with respect to the claim 1-13 have been fully considered but are not persuasive.
The applicant argues on page 1, “Chow fails to teach to deform object presence areas in two object presence images to generate two deformed images.”
In response, the office does not find this argument to be persuasive. Based on the breadth of the claim language the prior art by CHOW et al. (Patent No.: US 10, 032, 077 B1), explicitly teaches
deform object presence areas in two object presence images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [04], Line [29-36]-CHOW discloses the radar image processing system 118 comprises a SAR imaging module 202 that generates SAR images 204-206 of the scene 107 based upon the SAR data 108. These SAR images 204-206 comprise views of the same scene 107 taken at different times), in which one or more objects are present (Fig. 1. Column [03], Line [60-65]-CHOW discloses the radar image processing system 118 identifies vehicle tracks in the CCD image), obtained from each of two observed images to generate two deformed images (Fig. 3. Column [04], Line [37-53]-CHOW discloses the CCD module 208 processes a first SAR image 204 and a second SAR image 205 to generate a first CCD image 210, and processes a third SAR image 206 and the second SAR image 205 to generate a second CCD image 211, where the second CCD image 211 is temporally disjoint from the first CCD image 210 (wherein the third image is generated through joint pre-processing of the first and second temporally disjoint images through techniques such as principal component analysis (PCA), independent component analysis (ICA), computation of the normalized coherence product (NCP)). Additionally, Column [05], Line [52-67]-CHOW discloses the track identification module 216 comprises a segmentation module 302 that segments the input CCD image 214 into a plurality of CCD image chips 304 (wherein the chips may be of equal size or different size and/or spatial resolution)), based on a size of the object appearing in each of the two observed images (Fig. 7. Column [09], Line [22-41]-CHOW discloses the selected size of the chips will depend on the resolution of the initial CCD image, and is selected to mitigate the generation of artifacts in later stages of the methodology, while capturing large enough portions of the original CCD image to be able to identify features of interest. Inverse Radon transforms of each of the Radon transforms of the CCD image chips are calculated. At 712, and morphological erosion may be applied in order to reduce line artifacts resulting from the inverse Radon transform process (wherein the office considers morphological operations are a form of image deformation). Please also read Column [08], Line [18-43] and Column [09], Line [01-20]).
The applicant argues on page 2, “Chow fails to teach determining difference of the object between the two images.” and on page 3, “Chow fails to teach to generate an image capable of identifying the determined difference.”
In response, the office does not find this argument to be persuasive. Based on the breadth of the claim language the prior art by CHOW et al. (Patent No.: US 10, 032, 077 B1), explicitly teaches
generate a synthesized image (Fig. 3, #214 called CCD image. Column [05], Line [52-67]) by synthesizing the two deformed images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [07], Line [18-24]-CHOW discloses the image reconstruction module 310 then receives the plurality of Radon transforms 308 and performs an inverse Radon transform to each of the Radon transforms 308, generating new image chips and stitching them together to construct a final track detection image of the scene 107 depicted in the original SAR images 204-206, with identified tracks indicated in the track detection image. Please also see Fig. 4 and read Column [04], Line [37-53]), determine difference of the object between the two object presence images (Fig. 7. Column [05], Line [24-51]-CHOW discloses the track identification module 216, upon identifying vehicle tracks in the CCD image 214 (and thus the scene 107), assigns a classification to the tracks (e.g., according to various characteristics) and signifies this classification in the graphical indication 1. The classification can indicate that the vehicle tracks are of a first width (e.g., rather than a second width). In another example, the classification of vehicle tracks can indicate that the vehicle tracks are of a second width (e.g., rather than the first width). Please also read Column [04], Line [54-68] and claims 5 and 10-11), and generate an image capable of identifying the determined difference (Fig. 7. 26 Column [07], Line [18-47]-CHOW discloses the track identification module 216 can signify this classification by assigning a first color to tracks having the first width and assigning a second color to tracks having the second width in the graphical indication 120 (e.g., where the graphical indication 120 is an image of the scene 107). An analyst can then view the graphical indication 120 and see (at a glance) which tracks were made by, for example, a passenger vehicle with the first track width and which were made by a commercial or cargo vehicle with the second track width).
The applicant argues on page 3, “Accordingly, the pending claims are patentable.”
In response, the office does not find this argument to be persuasive for the reasons stated above and below. The office respectfully encourages the applicant to amend the claims to overcome the prior arts on record.
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 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 of this title, 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, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over CHOW (US 10032077 B1), hereinafter referenced as CHOW in view of HU et al. (WO 2020/233591 A1 Corresponding to US 20210011149 A1), hereinafter referenced as HU.
Regarding claim 1, CHOW explicitly teaches an image processing device (Fig. 1, #100 called a SAR system. Column [03], Line [53-55]. Further at Column [03], Line [04-06]-CHOW discloses the disclosure is directed to systems and methods for identifying vehicle tracks in synthetic aperture radar (SAR) coherent change detection (CCD) images) comprising:
a memory storing software instructions (Fig. 8, #804 called memory. Column [03], Line [53-55]), and one or more processors (Fig. 8, #802 called processors. Column [03], Line [53-55]) configured to execute the software instructions to deform object presence areas in two object presence images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [04], Line [29-36]-CHOW discloses the radar image processing system 118 comprises a SAR imaging module 202 that generates SAR images 204-206 of the scene 107 based upon the SAR data 108. These SAR images 204-206 comprise views of the same scene 107 taken at different times), in which one or more objects are present (Fig. 1. Column [03], Line [60-65]-CHOW discloses the radar image processing system 118 identifies vehicle tracks in the CCD image), obtained from each of two observed images to generate two deformed images (Fig. 3. Column [04], Line [37-53]-CHOW discloses the CCD module 208 processes a first SAR image 204 and a second SAR image 205 to generate a first CCD image 210, and processes a third SAR image 206 and the second SAR image 205 to generate a second CCD image 211, where the second CCD image 211 is temporally disjoint from the first CCD image 210 (wherein the third image is generated through joint pre-processing of the first and second temporally disjoint images through techniques such as principal component analysis (PCA), independent component analysis (ICA), computation of the normalized coherence product (NCP)). Additionally, Column [05], Line [52-67]-CHOW discloses the track identification module 216 comprises a segmentation module 302 that segments the input CCD image 214 into a plurality of CCD image chips 304 (wherein the chips may be of equal size or different size and/or spatial resolution)), based on a size of the object appearing in each of the two observed images (Fig. 7. Column [09], Line [22-41]-CHOW discloses the selected size of the chips will depend on the resolution of the initial CCD image, and is selected to mitigate the generation of artifacts in later stages of the methodology, while capturing large enough portions of the original CCD image to be able to identify features of interest. Inverse Radon transforms of each of the Radon transforms of the CCD image chips are calculated. At 712, and morphological erosion may be applied in order to reduce line artifacts resulting from the inverse Radon transform process (wherein the office considers morphological operations are a form of image deformation). Please also read Column [08], Line [18-43] and Column [09], Line [01-20]), and
generate a synthesized image (Fig. 3, #214 called CCD image. Column [05], Line [52-67]) by synthesizing the two deformed images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [07], Line [18-24]-CHOW discloses the image reconstruction module 310 then receives the plurality of Radon transforms 308 and performs an inverse Radon transform to each of the Radon transforms 308, generating new image chips and stitching them together to construct a final track detection image of the scene 107 depicted in the original SAR images 204-206, with identified tracks indicated in the track detection image. Please also see Fig. 4 and read Column [04], Line [37-53]), determine difference of the object between the two object presence images (Fig. 7. 20 Column [05], Line [24-51]-CHOW discloses the track identification module 216, upon identifying vehicle tracks in the CCD image 214 (and thus the scene 107), assigns a classification to the tracks (e.g., according to various characteristics) and signifies this classification in the graphical indication 1. The classification can indicate that the vehicle tracks are of a first width (e.g., rather than a second width). In another example, the classification of vehicle tracks can indicate that the vehicle tracks are of a second width (e.g., rather than the first width). Please also read Column [04], Line [54-68]), and generate an image capable of identifying the determined difference (Fig. 7. 26 Column [07], Line [18-47]-CHOW discloses the track identification module 216 can signify this classification by assigning a first color to tracks having the first width and assigning a second color to tracks having the second width in the graphical indication 120 (e.g., where the graphical indication 120 is an image of the scene 107). An analyst can then view the graphical indication 120 and see (at a glance) which tracks were made by, for example, a passenger vehicle with the first track width and which were made by a commercial or cargo vehicle with the second track width).
Chow fails to explicitly teach generate two deformed images, based on an observation angle of each of the two observed images.
However, HU explicitly teaches generate two deformed images, based on an observation angle of each of the two observed images (Fig. 1. Paragraph [0003]-HU discloses InSAR technology processes two SAR images of the same area at different times to obtain a one-dimensional average deformation result. GNSS technology uses a ground receiver to obtain a time-continuous three-dimensional coordinate sequence. Further in paragraph [0085]-HU discloses the prior variances of InSAR and GNSS observations are used to determine the weights, and the three-dimensional surface deformation is solved with the least square method. Additionally in paragraph [0084]-HU discloses the simulation data description includes (1) simulating the three-dimensional deformation field in east-west, north-south and vertical directions in a certain area (image size 400×450) (as shown in FIG. 2, (a)-(c)); (2) combining imaging geometry of sentinel-1A/B satellite data to calculate the ascending and descending InSAR deformation results, wherein the incident angle and the azimuth angle of the ascending orbit data are 39.3° and −12.2°, respectively; the incident angle and the azimuth angle of the descending orbit data are 33.9° and −167.8°, respectively” (wherein α.sub.i.sup.k, θ.sub.i.sup.k are respectively an azimuth angle and an incident angle of a satellite when acquiring the SAR data). Please also read paragraph [0057 and 0059]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW of having an image processing device comprising: a memory storing software instructions, and one or more processors configured to execute the software instructions to deform object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of HU of having generate two deformed images, based on an observation angle of each of the two observed images.
Wherein having CHOW’s image processing device having generate two deformed images, based on an observation angle of each of the two observed images.
The motivation behind the modification would have been to obtain an image processing method that improves the robustness and accuracy of data, since both CHOW and HU system analyze SAR data. Wherein CHOW’s system improves the robustness of data, while HU provides a weighting algorithm for fusing InSAR and GNSS that increases the accuracy and spatial resolution of data used to monitor three-dimensional surface deformation. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HU et al. (WO 2020233591 A1 Corresponding to US 20210011149 A1), Paragraph [0003-0004].
Regarding claim 6, CHOW explicitly teaches an image processing method (Fig. 1, #100 called a SAR system. Column [03], Line [53-55]. Further at Column [03], Line [04-06]-CHOW discloses the disclosure is directed to systems and methods for identifying vehicle tracks in synthetic aperture radar (SAR) coherent change detection (CCD) images), implemented by a processor (Fig. 8, #802 called processors. Column [03], Line [53-55]), comprising:
deforming object presence areas in two object presence images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Para 17 Column [00, Line [00]-CHOW discloses the radar image processing system 118 comprises a SAR imaging module 202 that generates SAR images 204-206 of the scene 107 based upon the SAR data 108. These SAR images 204-206 comprise views of the same scene 107 taken at different times), in which one or more objects are present (Fig. 1. Column [03], Line [60-65]-CHOW discloses the radar image processing system 118 identifies vehicle tracks in the CCD image), obtained from each of two observed images to generate two deformed images (Fig. 3. Column [04], Line [37-53]-CHOW discloses the CCD module 208 processes a first SAR image 204 and a second SAR image 205 to generate a first CCD image 210, and processes a third SAR image 206 and the second SAR image 205 to generate a second CCD image 211, where the second CCD image 211 is temporally disjoint from the first CCD image 210 (wherein the third image is generated through joint pre-processing of the first and second temporally disjoint images through techniques such as principal component analysis (PCA), independent component analysis (ICA), computation of the normalized coherence product (NCP)). Additionally, Column [05], Line [52-67]-CHOW discloses the track identification module 216 comprises a segmentation module 302 that segments the input CCD image 214 into a plurality of CCD image chips 304 (wherein the chips may be of equal size or different size and/or spatial resolution)), based on a size of the object appearing in each of the two observed images (Fig. 7. Column [09], Line [22-41]-CHOW discloses the selected size of the chips will depend on the resolution of the initial CCD image, and is selected to mitigate the generation of artifacts in later stages of the methodology, while capturing large enough portions of the original CCD image to be able to identify features of interest. Inverse Radon transforms of each of the Radon transforms of the CCD image chips are calculated. At 712, and morphological erosion may be applied in order to reduce line artifacts resulting from the inverse Radon transform process (wherein the office considers morphological operations are a form of image deformation). Please also read Column [08], Line [18-43] and Column [09], Line [01-20]), and generating a synthesized image by synthesizing the two deformed images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [07], Line [18-24]-CHOW discloses the image reconstruction module 310 then receives the plurality of Radon transforms 308 and performs an inverse Radon transform to each of the Radon transforms 308, generating new image chips and stitching them together to construct a final track detection image of the scene 107 depicted in the original SAR images 204-206, with identified tracks indicated in the track detection image. Please also see Fig. 4 and read Column [04], Line [37-53]), determining difference of the object between the two object presence images (Fig. 7. 20 Column [05], Line [24-51]-CHOW discloses the track identification module 216, upon identifying vehicle tracks in the CCD image 214 (and thus the scene 107), assigns a classification to the tracks (e.g., according to various characteristics) and signifies this classification in the graphical indication 1. The classification can indicate that the vehicle tracks are of a first width (e.g., rather than a second width). In another example, the classification of vehicle tracks can indicate that the vehicle tracks are of a second width (e.g., rather than the first width). Please also read Column [04], Line [54-68]), and generating an image capable of identifying the determined difference (Fig. 7. 26 Column [07], Line [18-47]-CHOW discloses the track identification module 216 can signify this classification by assigning a first color to tracks having the first width and assigning a second color to tracks having the second width in the graphical indication 120 (e.g., where the graphical indication 120 is an image of the scene 107). An analyst can then view the graphical indication 120 and see (at a glance) which tracks were made by, for example, a passenger vehicle with the first track width and which were made by a commercial or cargo vehicle with the second track width).
Chow fails to explicitly teach generate two deformed images, based on an observation angle of each of the two observed images.
However, HU explicitly teaches generate two deformed images, based on an observation angle of each of the two observed images (Fig. 1. Paragraph [0003]-HU discloses InSAR technology processes two SAR images of the same area at different times to obtain a one-dimensional average deformation result. GNSS technology uses a ground receiver to obtain a time-continuous three-dimensional coordinate sequence. Further in paragraph [0085]-HU discloses the prior variances of InSAR and GNSS observations are used to determine the weights, and the three-dimensional surface deformation is solved with the least square method. Additionally in paragraph [0084]-HU discloses the simulation data description includes (1) simulating the three-dimensional deformation field in east-west, north-south and vertical directions in a certain area (image size 400×450) (as shown in FIG. 2, (a)-(c)); (2) combining imaging geometry of sentinel-1A/B satellite data to calculate the ascending and descending InSAR deformation results, wherein the incident angle and the azimuth angle of the ascending orbit data are 39.3° and −12.2°, respectively; the incident angle and the azimuth angle of the descending orbit data are 33.9° and −167.8°, respectively” (wherein α.sub.i.sup.k, θ.sub.i.sup.k are respectively an azimuth angle and an incident angle of a satellite when acquiring the SAR data). Please also read paragraph [0057 and 0059]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW of having an image processing method, implemented by a processor, comprising: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, based on a size of the object appearing in each of the two observed images, and generating a synthesized image by synthesizing the two deformed images, with the teachings of HU of having generate two deformed images, based on an observation angle of each of the two observed images.
Wherein having CHOW’s an image processing method having generate two deformed images, based on an observation angle of each of the two observed images.
The motivation behind the modification would have been to obtain an image processing method that improves the robustness and accuracy of data, since both CHOW and HU system analyze SAR data. Wherein CHOW’s system improves the robustness of data, while HU provides a weighting algorithm for fusing InSAR and GNSS that increases the accuracy and spatial resolution of data used to monitor three-dimensional surface deformation. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HU et al. (WO 2020233591 A1 Corresponding to US 20210011149 A1), Paragraph [0003-0004].
Regarding claim 10, CHOW explicitly teaches a non-transitory computer readable recording medium (Fig. 8, #804 called memory. Column [03], Line [53-55]. Please also see Column [10], Line [39-65]) storing an image processing program (Fig. 1, #100 called a SAR system. Column [03], Line [53-55]. Further at Column [03], Line [04-06]-CHOW discloses the disclosure is directed to systems and methods for identifying vehicle tracks in synthetic aperture radar (SAR) coherent change detection (CCD) images) which, when executed by a processor (Fig. 8, #802 called processors. Column [03], Line [53-55]), performs:
deforming object presence areas in two object presence images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [04], Line [29-36]-CHOW discloses the radar image processing system 118 comprises a SAR imaging module 202 that generates SAR images 204-206 of the scene 107 based upon the SAR data 108. These SAR images 204-206 comprise views of the same scene 107 taken at different times), in which one or more objects are present (Fig. 1. Column [03], Line [60-65]-CHOW discloses the radar image processing system 118 identifies vehicle tracks in the CCD image), obtained from each of two observed images to generate two deformed images (Fig. 3. Column [04], Line [37-53]-CHOW discloses the CCD module 208 processes a first SAR image 204 and a second SAR image 205 to generate a first CCD image 210, and processes a third SAR image 206 and the second SAR image 205 to generate a second CCD image 211, where the second CCD image 211 is temporally disjoint from the first CCD image 210 (wherein the third image is generated through joint pre-processing of the first and second temporally disjoint images through techniques such as principal component analysis (PCA), independent component analysis (ICA), computation of the normalized coherence product (NCP)). Additionally, Column [05], Line [52-67]-CHOW discloses the track identification module 216 comprises a segmentation module 302 that segments the input CCD image 214 into a plurality of CCD image chips 304 (wherein the chips may be of equal size or different size and/or spatial resolution)), based on a size of the object appearing in each of the two observed images (Fig. 7. Column [09], Line [22-41]-CHOW discloses the selected size of the chips will depend on the resolution of the initial CCD image, and is selected to mitigate the generation of artifacts in later stages of the methodology, while capturing large enough portions of the original CCD image to be able to identify features of interest. Inverse Radon transforms of each of the Radon transforms of the CCD image chips are calculated. At 712, and morphological erosion may be applied in order to reduce line artifacts resulting from the inverse Radon transform process (wherein the office considers morphological operations are a form of image deformation). Please also read Column [08], Line [18-43] and Column [09], Line [01-20]), and generating a synthesized image (Fig. 3, #214 called CCD image. Column [05], Line [52-67]) by synthesizing the two deformed images (Fig. 3, #204, #206, called CCD IMG. Column [04], Line [29-36]. Further at Column [07], Line [18-24]-CHOW discloses the image reconstruction module 310 then receives the plurality of Radon transforms 308 and performs an inverse Radon transform to each of the Radon transforms 308, generating new image chips and stitching them together to construct a final track detection image of the scene 107 depicted in the original SAR images 204-206, with identified tracks indicated in the track detection image. Please also see Fig. 4 and read Column [04], Line [37-53]), determining difference of the object between the two object presence images (Fig. 7. 20 Column [05], Line [24-51]-CHOW discloses the track identification module 216, upon identifying vehicle tracks in the CCD image 214 (and thus the scene 107), assigns a classification to the tracks (e.g., according to various characteristics) and signifies this classification in the graphical indication 1. The classification can indicate that the vehicle tracks are of a first width (e.g., rather than a second width). In another example, the classification of vehicle tracks can indicate that the vehicle tracks are of a second width (e.g., rather than the first width). Please also read Column [04], Line [54-68]), and generating an image capable of identifying the determined difference (Fig. 7. 26 Column [07], Line [18-47]-CHOW discloses the track identification module 216 can signify this classification by assigning a first color to tracks having the first width and assigning a second color to tracks having the second width in the graphical indication 120 (e.g., where the graphical indication 120 is an image of the scene 107). An analyst can then view the graphical indication 120 and see (at a glance) which tracks were made by, for example, a passenger vehicle with the first track width and which were made by a commercial or cargo vehicle with the second track width).
Chow fails to explicitly teach generate two deformed images, based on an observation angle of each of the two observed images.
However, HU explicitly teaches generate two deformed images, based on an observation angle of each of the two observed images (Fig. 1. Paragraph [0003]-HU discloses InSAR technology processes two SAR images of the same area at different times to obtain a one-dimensional average deformation result. GNSS technology uses a ground receiver to obtain a time-continuous three-dimensional coordinate sequence. Further in paragraph [0085]-HU discloses the prior variances of InSAR and GNSS observations are used to determine the weights, and the three-dimensional surface deformation is solved with the least square method. Additionally in paragraph [0084]-HU discloses the simulation data description includes (1) simulating the three-dimensional deformation field in east-west, north-south and vertical directions in a certain area (image size 400×450) (as shown in FIG. 2, (a)-(c)); (2) combining imaging geometry of sentinel-1A/B satellite data to calculate the ascending and descending InSAR deformation results, wherein the incident angle and the azimuth angle of the ascending orbit data are 39.3° and −12.2°, respectively; the incident angle and the azimuth angle of the descending orbit data are 33.9° and −167.8°, respectively” (wherein α.sub.i.sup.k, θ.sub.i.sup.k are respectively an azimuth angle and an incident angle of a satellite when acquiring the SAR data). Please also read paragraph [0057 and 0059]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW of having a non-transitory computer readable recording medium storing an image processing program which, when executed by a processor, performs: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of HU of having generate two deformed images, based on an observation angle of each of the two observed images.
Wherein having CHOW’s image processing device having generate two deformed images, based on an observation angle of each of the two observed images.
The motivation behind the modification would have been to obtain an image processing device that improves the robustness and accuracy of data, since both CHOW and HU system analyze SAR data. Wherein CHOW’s system improves the robustness of data, while HU provides a weighting algorithm for fusing InSAR and GNSS that increases the accuracy and spatial resolution of data used to monitor three-dimensional surface deformation. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HU et al. (WO 2020233591 A1 Corresponding to US 20210011149 A1), Paragraph [0003-0004].
Claims 2, 7 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over CHOW (US 10032077 B1), hereinafter referenced as CHOW in view of HU et al. (US 20210011149 A1), hereinafter referenced as HU and in further view of AJADI et al. (Ajadi et al. “Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach”, Remote Sensing 8(6):482. https://doi.org/10.3390/rs8060482. 2016. Year: 2016), hereinafter referenced as AJADI.
Regarding claim 2, CHOW in view of HU explicitly teach the image processing device according to claim 1, CHOW in view of HU fail to explicitly teach wherein the one or more processors are configured to execute the software instructions to dilate the object presence area by a predetermined amount in each of the two object presence images.
However, AJADI explicitly teaches wherein the one or more processors are configured to execute the software instructions to dilate the object presence area by a predetermined amount (Fig. 1. Page 9, 2nd Paragraph-AJADI discloses morphological filters are defined by structuring element (S), which is based on a moving window of a given size and shape centered on a pixel XLR (wherein the optimal shape and size was found to be a fixed squared shape of 20 x 20 pixels). The two morphological filters used are opening and closing and they are a concatenation of erosion and dilation (wherein closing by reconstruction is dilation followed by a series of erosions). Please also read Page 9, 2nd through 5th Paragraphs) in each of the two object presence images (Fig. 1, called Xn SAR images, Xi images and Xr reference images. Page 1, 2nd Paragraph. Please also read page 5, 2nd Paragraph and page 13, 2nd paragraph).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU of having an image processing device comprising: a memory storing software instructions, and one or more processors configured to execute the software instructions to deform object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of AJADI of having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area by a predetermined amount.
Wherein having CHOW’s image processing device having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area by a predetermined amount.
The motivation behind the modification would have been to obtain an image processing device that improves performance and data robustness across a wide range of spatial scales, since both CHOW and AJADI systems analyze SAR data. Wherein CHOW’s system improves the robustness of data, while AJADI provides automatic and high-performance change detection across a wide range of spatial scales (resolution levels). Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and AJADI et al. (Ajadi et al. “Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach”, Remote Sensing 8(6):482. https://doi.org/10.3390/rs8060482. 2016. Year: 2016), Abstract and Page 25, 2nd through 3rd Paragraph.
Regarding claim 7, CHOW in view of HU explicitly teach the image processing method, implemented by a processor, according to claim 6, CHOW in view of HU fail to explicitly teach wherein the object presence area is dilated by a predetermined amount in each of the two object presence images.
However, AJADI explicitly teaches wherein the object presence area is dilated by a predetermined amount (Fig. 1. Page 9, 2nd Paragraph-AJADI discloses morphological filters are defined by structuring element (S), which is based on a moving window of a given size and shape centered on a pixel XLR (wherein the optimal shape and size was found to be a fixed squared shape of 20 x 20 pixels). The two morphological filters used are opening and closing and they are a concatenation of erosion and dilation (wherein closing by reconstruction is dilation followed by a series of erosions). Please also read Page 9, 2nd through 5th Paragraphs) in each of the two object presence images (Fig. 1, called Xn SAR images, Xi images and Xr reference images. Page 1, 2nd Paragraph. Please also read page 5, 2nd Paragraph and page 13, 2nd paragraph).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU of having an image processing method, implemented by a processor, comprising: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of AJADI of having wherein the object presence area is dilated by a predetermined amount in each of the two object presence images.
Wherein having CHOW’s image processing method having wherein the object presence area is dilated by a predetermined amount in each of the two object presence images.
The motivation behind the modification would have been to obtain an image processing device that improves performance and data robustness across a wide range of spatial scales, since both CHOW and AJADI systems analyze SAR data. Wherein CHOW’s system improves the robustness of data, while AJADI provides automatic and high-performance change detection across a wide range of spatial scales (resolution levels). Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and AJADI et al. (Ajadi et al. “Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach”, Remote Sensing 8(6):482. https://doi.org/10.3390/rs8060482. 2016. Year: 2016), Abstract and Page 25, 2nd through 3rd Paragraph.
Regarding claim 11, CHOW in view of HU explicitly teach the non-transitory computer readable recording medium according to claim 10, CHOW in view of HU fail to explicitly teach wherein the image processing program performs dilating the object presence area by a predetermined amount in each of the two object presence images.
However, AJADI explicitly teaches wherein the image processing program performs dilating the object presence area by a predetermined amount (Fig. 1. Page 9, 2nd Paragraph-AJADI discloses morphological filters are defined by structuring element (S), which is based on a moving window of a given size and shape centered on a pixel XLR (wherein the optimal shape and size was found to be a fixed squared shape of 20 x 20 pixels). The two morphological filters used are opening and closing and they are a concatenation of erosion and dilation (wherein closing by reconstruction is dilation followed by a series of erosions). Please also read Page 9, 2nd through 5th Paragraphs) in each of the two object presence images (Fig. 1, called Xn SAR images, Xi images and Xr reference images. Page 1, 2nd Paragraph. Please also read page 5, 2nd Paragraph and page 13, 2nd paragraph).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU of having a non-transitory computer readable recording medium storing an image processing program which, when executed by a processor, performs: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of AJADI of having wherein the image processing program performs dilating the object presence area by a predetermined amount in each of the two object presence images.
Wherein having CHOW’s image processing device having wherein the image processing program performs dilating the object presence area by a predetermined amount in each of the two object presence images.
The motivation behind the modification would have been to obtain an image processing device that improves performance and data robustness across a wide range of spatial scales, since both CHOW and AJADI systems analyze SAR data. Wherein CHOW’s system improves the robustness of data, while AJADI provides automatic and high-performance change detection across a wide range of spatial scales (resolution levels). Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and AJADI et al. (Ajadi et al. “Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach”, Remote Sensing 8(6):482. https://doi.org/10.3390/rs8060482. 2016. Year: 2016), Abstract and Page 25, 2nd through 3rd Paragraph.
Claims 5, 9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over CHOW (US 10032077 B1), hereinafter referenced as CHOW in view of HU et al. (US 20210011149 A1), hereinafter referenced as HU and in further view of BERNAL et al. (US 20150131851 A1), hereinafter referenced as BERNAL.
Regarding claim 5, CHOW in view of HU explicitly teach the image processing device according to claim 1, CHOW in view of HU fail to explicitly teach wherein the one or more processors are configured to further execute the software instructions to eliminate areas whose sizes are smaller than a predetermined value determined based on a width of the object
However, BERNAL explicitly teaches wherein the one or more processors are configured to further execute the software instructions to eliminate areas whose sizes are smaller than a predetermined value determined based on a width of the object (Fig. 2. Paragraph [0044]-BERNAL discloses the size and orientation determination unit 118 (which is aware of the predominant object size and orientation of an object 140 as a function of location) creates the required structuring elements 164 for the morphological operations related with the computation of the foreground/motion binary mask, e.g., 404, 410, 416. The morphological operations perform hole-filling in masks that result from the initial thresholding operation, as well as removal of identified objects with sizes and/or orientations outside a pre-specified range depending on the object location. A structuring element 164 of a given width and height can be used as an erosion or opening element on a binary mask 142 containing identified foreground or moving objects so that objects with width or height smaller than those of the structuring element 164 will be eliminated from the mask 142).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU of having an image processing device comprising: a memory storing software instructions, and one or more processors configured to execute the software instructions to deform object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of BERNAL of having wherein the one or more processors are configured to further execute the software instructions to eliminate areas whose sizes are smaller than a predetermined value determined based on a width of the object.
Wherein having CHOW’s image processing device having wherein the one or more processors are configured to further execute the software instructions to eliminate areas whose sizes are smaller than a predetermined value determined based on a width of the object.
The motivation behind the modification would have been to obtain an image processing device that improves efficient object tracking and data robustness, since both CHOW and BERNAL systems are used for the analysis of image data. Wherein CHOW’s system improves the robustness of data, while BERNAL system achieves robust and computationally efficient tracking. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and BERNAL et al. (US 20150131851 A1), Abstract and Paragraph [0035].
Regarding claim 9, CHOW in view of HU explicitly teach the image processing method, implemented by a processor, according to claim 6, CHOW in view of HU fail to explicitly teach further comprising eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object.
However, BERNAL explicitly teaches further comprising eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object (Fig. 2. Paragraph [0044]-BERNAL discloses the size and orientation determination unit 118 (which is aware of the predominant object size and orientation of an object 140 as a function of location) creates the required structuring elements 164 for the morphological operations related with the computation of the foreground/motion binary mask, e.g., 404, 410, 416. The morphological operations perform hole-filling in masks that result from the initial thresholding operation, as well as removal of identified objects with sizes and/or orientations outside a pre-specified range depending on the object location. A structuring element 164 of a given width and height can be used as an erosion or opening element on a binary mask 142 containing identified foreground or moving objects so that objects with width or height smaller than those of the structuring element 164 will be eliminated from the mask 142).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU of an image processing method, implemented by a processor, comprising: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, based on an observation angle of each of the two observed images and a size of the object appearing in each of the two observed images, and generating a synthesized image by synthesizing the two deformed images, determining difference of the object between the two object presence images, and generating an image capable of identifying the determined difference, with the teachings of BERNAL of having further comprising eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object.
Wherein having CHOW’s image processing method having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area by a predetermined amount.
The motivation behind the modification would have been to obtain an image processing device that improves efficient object tracking and data robustness, since both CHOW and BERNAL systems are used for the analysis of image data. Wherein CHOW’s system improves the robustness of data, while BERNAL system achieves robust and computationally efficient tracking. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and BERNAL et al. (US 20150131851 A1), Abstract and Paragraph [0035].
Regarding claim 13, CHOW in view of HU explicitly teach the recording medium according to claim10, CHOW in view of HU fail to explicitly teach wherein the image processing program performs eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object.
However, BERNAL explicitly teaches wherein the image processing program performs eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object (Fig. 2. Paragraph [0044]-BERNAL discloses the size and orientation determination unit 118 (which is aware of the predominant object size and orientation of an object 140 as a function of location) creates the required structuring elements 164 for the morphological operations related with the computation of the foreground/motion binary mask, e.g., 404, 410, 416. The morphological operations perform hole-filling in masks that result from the initial thresholding operation, as well as removal of identified objects with sizes and/or orientations outside a pre-specified range depending on the object location. A structuring element 164 of a given width and height can be used as an erosion or opening element on a binary mask 142 containing identified foreground or moving objects so that objects with width or height smaller than those of the structuring element 164 will be eliminated from the mask 142).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU of having a non-transitory computer readable recording medium storing an image processing program which, when executed by a processor, performs: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of BERNAL of having wherein the image processing program performs eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object.
Wherein having CHOW’s image processing device having wherein the image processing program performs eliminating areas whose sizes are smaller than a predetermined value determined based on a width of the object.
The motivation behind the modification would have been to obtain an image processing device that improves efficient object tracking and data robustness, since both CHOW and BERNAL systems are used for the analysis of image data. Wherein CHOW’s system improves the robustness of data, while BERNAL system achieves robust and computationally efficient tracking. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and BERNAL et al. (US 20150131851 A1), Abstract and Paragraph [0035].
Claims 3, 8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over CHOW (US 10032077 B1), hereinafter referenced as CHOW in view of HU et al. (US 20210011149 A1), hereinafter referenced as HU and in further view of AJADI et al. (Ajadi et al. “Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach”, Remote Sensing 8(6):482. https://doi.org/10.3390/rs8060482. 2016. Year: 2016), hereinafter referenced as AJADI and in further view of HAINLINE et al. (US 10553020 B1), hereinafter referenced as HAINLINE
Regarding claim 3, CHOW in view of HU and in further view of AJADI explicitly teach the image processing device according to claim 2, CHOW in view of HU and in further view of AJADI fail to explicitly teach wherein the one or more processors are configured to execute the software instructions to dilate the object presence area in a first object presence image of the two object presence images in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and dilate the object presence area in the second object presence image in accordance with collapsing direction and collapse amount of the object in the first object presence image.
However, HAINLINE explicitly teaches wherein the one or more processors are configured to execute the software instructions to dilate (Fig. 9. Column [09], Line [48-54]-HAINLINE discloses at operation 806, a dilation filter can be applied (e.g., iteratively) to the aggressive shadow mask. Further at Column [09], Line [55-57]-HAINLINE discloses at operation 808, an erosion filter can be applied (e.g., iteratively) (wherein each filter assigns a region as shadowed or non-shadowed and the number of iterations of dilation and erosion can be set to the number of pixels in error in the shadow mask as computed by operation 802 and 804)) the object presence area in a first object presence image (Fig. 9. Column [05], Line [12-16]-HAINLINE discloses the image 218 is a rendered view of an embodiment of the image data 106. The image 218 includes an object with shadows. The direction of solar rays from the sun is indicated by the solar ray 216) of the two object presence images (Fig. 9. Column [02], Line [13-17]-HAINLINE discloses embodiments relate to generating context masks for images. Context masks can be for one or more shadows of one or more images) in accordance with collapsing direction (Fig. 9. Column [07], Line [06-12]-HAINLINE discloses the operation 404 can include computing parameters, such as solar ray angle and solar ray azimuth angle, for setting up the solar-oriented coordinate system) and collapse amount (Fig. 9. Column [09], Line [28-39]-HAINLINE discloses the operation 412 at operation 802, projecting x, y, and z-dimension error from the elevation data to the input image space to obtain max pixels in error. At operation 804, a contribution to the error of the mapping function (mapping the solar coordinate system to the image coordinate system) can be added to error determined at operation 802. The elevation error from operation 802 can be used to compute an effect on the projected shadow length by multiplying by the tangent of the sun elevation angle. This can provide an x-y offset in ground space that can then be mapped to input image space to get a pixel-based offset) of the object in a second object presence image of the two object presence images, and
dilate the object presence area in the second object presence image (Fig. 9. Column [09], Line [48-54]-HAINLINE discloses at operation 806, a dilation filter can be applied (e.g., iteratively) to the aggressive shadow mask. Further at Column [09], Line [55-57]-HAINLINE discloses at operation 808, an erosion filter can be applied (e.g., iteratively) (wherein each filter assigns a region as shadowed or non-shadowed and the number of iterations of dilation and erosion can be set to the number of pixels in error in the shadow mask as computed by operation 802 and 804)) in accordance with collapsing direction (Fig. 9. Column [07], Line [06-12]-HAINLINE discloses the operation 404 can include computing parameters, such as solar ray angle and solar ray azimuth angle, for setting up the solar-oriented coordinate system) and collapse amount (Fig. 9. Column [09], Line [28-39]-HAINLINE discloses the operation 412 at operation 802, projecting x, y, and z-dimension error from the elevation data to the input image space to obtain max pixels in error. At operation 804, a contribution to the error of the mapping function (mapping the solar coordinate system to the image coordinate system) can be added to error determined at operation 802. The elevation error from operation 802 can be used to compute an effect on the projected shadow length by multiplying by the tangent of the sun elevation angle. This can provide an x-y offset in ground space that can then be mapped to input image space to get a pixel-based offset) of the object in the first object presence image ((Fig. 9. Column [05], Line [12-16]-HAINLINE discloses the image 218 is a rendered view of an embodiment of the image data 106. The image 218 includes an object with shadows. Column [02], Line [13-17]-HAINLINE discloses embodiments relate to generating context masks for images. Context masks can be for one or more shadows of one or more images)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU and in further view of AJADI of having an image processing device comprising: a memory storing software instructions, and one or more processors configured to execute the software instructions to deform object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of HAINLINE of having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area in a first object presence image of the two object presence images in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and dilate the object presence area in the second object presence image in accordance with collapsing direction and collapse amount of the object in the first object presence image.
Wherein having CHOW’s image processing device having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area in a first object presence image of the two object presence images in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and dilate the object presence area in the second object presence image in accordance with collapsing direction and collapse amount of the object in the first object presence image.
The motivation behind the modification would have been to obtain an image processing method that improves the accuracy of shadow masks and data robustness, since both CHOW and HAINLINE identify and analyze regions in aerial images. Wherein CHOW’s system improves the robustness of data, while HAINLINE system improves the accuracy of masks for determining whether a pixel is shadow or a non-shadow. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HAINLINE et al. (US 10553020 B1), Abstract and Column [02], Line [30-45].
Regarding claim 8, CHOW in view of HU and in further view of AJADI explicitly teach the image processing method, implemented by a processor, according to claim 7, CHOW in view of HU and in further view of AJADI fail to explicitly teach wherein the object presence area in a first object presence image of the two object presence images is dilated in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and the object presence area in the second object presence image is dilated in accordance with collapsing direction and collapse amount of the object in the first object presence image.
However, HAINLINE explicitly teaches wherein the object presence area in a first object presence image of the two object presence images (Fig. 9. Column [02], Line [13-17]-HAINLINE discloses embodiments relate to generating context masks for images. Context masks can be for one or more shadows of one or more images) is dilated (Fig. 9. Column [09], Line [48-54]-HAINLINE discloses at operation 806, a dilation filter can be applied (e.g., iteratively) to the aggressive shadow mask. Further at Column [09], Line [55-57]-HAINLINE discloses at operation 808, an erosion filter can be applied (e.g., iteratively) (wherein each filter assigns a region as shadowed or non-shadowed and the number of iterations of dilation and erosion can be set to the number of pixels in error in the shadow mask as computed by operation 802 and 804)) in accordance with collapsing direction (Fig. 9. Column [07], Line [06-12]-HAINLINE discloses the operation 404 can include computing parameters, such as solar ray angle and solar ray azimuth angle, for setting up the solar-oriented coordinate system) and collapse amount of the object in a second object presence image of the two object presence images (Fig. 9. Column [09], Line [28-39]-HAINLINE discloses the operation 412 at operation 802, projecting x, y, and z-dimension error from the elevation data to the input image space to obtain max pixels in error. At operation 804, a contribution to the error of the mapping function (mapping the solar coordinate system to the image coordinate system) can be added to error determined at operation 802. The elevation error from operation 802 can be used to compute an effect on the projected shadow length by multiplying by the tangent of the sun elevation angle. This can provide an x-y offset in ground space that can then be mapped to input image space to get a pixel-based offset), and
the object presence area in the second object presence image is dilated (Fig. 9. Column [09], Line [48-54]-HAINLINE discloses at operation 806, a dilation filter can be applied (e.g., iteratively) to the aggressive shadow mask. Further at Column [09], Line [55-57]-HAINLINE discloses at operation 808, an erosion filter can be applied (e.g., iteratively) (wherein each filter assigns a region as shadowed or non-shadowed and the number of iterations of dilation and erosion can be set to the number of pixels in error in the shadow mask as computed by operation 802 and 804)) in accordance with collapsing direction (Fig. 9. Column [07], Line [06-12]-HAINLINE discloses the operation 404 can include computing parameters, such as solar ray angle and solar ray azimuth angle, for setting up the solar-oriented coordinate system) and collapse amount of the object in the first object presence image (Fig. 9. Column [09], Line [28-39]-HAINLINE discloses the operation 412 at operation 802, projecting x, y, and z-dimension error from the elevation data to the input image space to obtain max pixels in error. At operation 804, a contribution to the error of the mapping function (mapping the solar coordinate system to the image coordinate system) can be added to error determined at operation 802. The elevation error from operation 802 can be used to compute an effect on the projected shadow length by multiplying by the tangent of the sun elevation angle. This can provide an x-y offset in ground space that can then be mapped to input image space to get a pixel-based offset).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU and in further view of AJADI of having an image processing method, implemented by a processor, comprising: deforming object presence areas in two object presence images, in which one or more objects are present, obtained from each of two observed images to generate two deformed images, with the teachings of HAINLINE of having wherein the object presence area in a first object presence image of the two object presence images is dilated in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and the object presence area in the second object presence image is dilated in accordance with collapsing direction and collapse amount of the object in the first object presence image.
Wherein having CHOW’s image processing method having wherein the object presence area in a first object presence image of the two object presence images is dilated in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and the object presence area in the second object presence image is dilated in accordance with collapsing direction and collapse amount of the object in the first object presence image.
The motivation behind the modification would have been to obtain an image processing method that improves the accuracy of shadow masks and data robustness, since both CHOW and HAINLINE identify and analyze regions in aerial images. Wherein CHOW’s system improves the robustness of data, while HAINLINE system improves the accuracy of masks for determining whether a pixel is shadow or a non-shadow. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HAINLINE et al. (US 10553020 B1), Abstract and Column [02], Line [30-45].
Regarding claim 12, CHOW in view of HU and in further view of AJADI explicitly teach The recording medium according to claim 11, CHOW in view of HU and in further view of AJADI fail to explicitly teach wherein the image processing program performs dilating the object presence area in a first object presence image of the two object presence images in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and dilating the object presence area in the second object presence image in accordance with collapsing direction and collapse amount of the object in the first object presence image.
However, HAINLINE explicitly teaches wherein the image processing program performs dilating the object presence area (Fig. 9. Column [09], Line [48-54]-HAINLINE discloses at operation 806, a dilation filter can be applied (e.g., iteratively) to the aggressive shadow mask. Further at Column [09], Line [55-57]-HAINLINE discloses at operation 808, an erosion filter can be applied (e.g., iteratively) (wherein each filter assigns a region as shadowed or non-shadowed and the number of iterations of dilation and erosion can be set to the number of pixels in error in the shadow mask as computed by operation 802 and 804)) in a first object presence image (Fig. 9. Column [05], Line [12-16]-HAINLINE discloses the image 218 is a rendered view of an embodiment of the image data 106. The image 218 includes an object with shadows. The direction of solar rays from the sun is indicated by the solar ray 216) of the two object presence images (Fig. 9. Column [02], Line [13-17]-HAINLINE discloses embodiments relate to generating context masks for images. Context masks can be for one or more shadows of one or more images) in accordance with collapsing direction (Fig. 9. Column [07], Line [06-12]-HAINLINE discloses the operation 404 can include computing parameters, such as solar ray angle and solar ray azimuth angle, for setting up the solar-oriented coordinate system) and collapse amount of the object in a second object presence image of the two object presence images (Fig. 9. Column [09], Line [28-39]-HAINLINE discloses the operation 412 at operation 802, projecting x, y, and z-dimension error from the elevation data to the input image space to obtain max pixels in error. At operation 804, a contribution to the error of the mapping function (mapping the solar coordinate system to the image coordinate system) can be added to error determined at operation 802. The elevation error from operation 802 can be used to compute an effect on the projected shadow length by multiplying by the tangent of the sun elevation angle. This can provide an x-y offset in ground space that can then be mapped to input image space to get a pixel-based offset), and
dilating the object presence area in the second object presence image (Fig. 9. Column [09], Line [48-54]-HAINLINE discloses at operation 806, a dilation filter can be applied (e.g., iteratively) to the aggressive shadow mask. Further at Column [09], Line [55-57]-HAINLINE discloses at operation 808, an erosion filter can be applied (e.g., iteratively) (wherein each filter assigns a region as shadowed or non-shadowed and the number of iterations of dilation and erosion can be set to the number of pixels in error in the shadow mask as computed by operation 802 and 804)) in accordance with collapsing direction (Fig. 9. Column [07], Line [06-12]-HAINLINE discloses the operation 404 can include computing parameters, such as solar ray angle and solar ray azimuth angle, for setting up the solar-oriented coordinate system) and collapse amount of the object in the first object presence image (Fig. 9. Column [09], Line [28-39]-HAINLINE discloses the operation 412 at operation 802, projecting x, y, and z-dimension error from the elevation data to the input image space to obtain max pixels in error. At operation 804, a contribution to the error of the mapping function (mapping the solar coordinate system to the image coordinate system) can be added to error determined at operation 802. The elevation error from operation 802 can be used to compute an effect on the projected shadow length by multiplying by the tangent of the sun elevation angle. This can provide an x-y offset in ground space that can then be mapped to input image space to get a pixel-based offset).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU and in further view of AJADI of having a non-transitory computer readable recording medium storing an image processing program which, when executed by a processor, performs: deforming object presence areas in two object presence images, in which one or more objects are present, with the teachings of HAINLINE of having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area in a first object presence image of the two object presence images in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and dilate the object presence area in the second object presence image in accordance with collapsing direction and collapse amount of the object in the first object presence image.
Wherein having CHOW’s image processing device having wherein the one or more processors are configured to execute the software instructions to dilate the object presence area in a first object presence image of the two object presence images in accordance with collapsing direction and collapse amount of the object in a second object presence image of the two object presence images, and dilate the object presence area in the second object presence image in accordance with collapsing direction and collapse amount of the object in the first object presence image.
The motivation behind the modification would have been to obtain an image processing device that improves the accuracy of shadow masks and data robustness, since both CHOW and HAINLINE identify and analyze regions in aerial images. Wherein CHOW’s improves the robustness of data, while HAINLINE improves the accuracy of masks for determining whether a pixel is shadow or a non-shadow. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HAINLINE et al. (US 10553020 B1), Abstract and Column [02], Line [30-45].
Claims 4 is rejected under 35 U.S.C. 103 as being unpatentable over CHOW (US 10032077 B1), hereinafter referenced as CHOW in view of HU et al. (US 20210011149 A1), hereinafter referenced as HU and in further view of AJADI et al. (Ajadi et al. “Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach”, Remote Sensing 8(6):482. https://doi.org/10.3390/rs8060482. 2016. Year: 2016), hereinafter referenced as AJADI and in further view of HAINLINE et al. (US 10553020 B1), hereinafter referenced as HAINLINE and in further view of KLARIC et al. (US 20100100835 A1), hereinafter referenced as KLARIC.
Regarding claim 4, CHOW in view of HU and in further view of AJADI explicitly teach the image processing device according to claim 3, CHOW in view of HU and in further view of AJADI and in further view of HAINLINE fail to explicitly teach wherein the one or more processors are configured to further execute the software instructions to calculate the collapse amount using the observation angle and a height of the object included in metadata of the two observed images, and determine the collapsing direction based on a observation direction included in the metadata of the observed image.
However, KLARIC explicitly teaches wherein the one or more processors are configured to further execute the software instructions to calculate the collapse amount (Fig. 5B. Paragraph [0120]-KLARIC discloses a differential morphological profile (DMP) is also created in a step 552. The DMP is an illustrative method that can be used to efficiently perform image segmentation, which basically divides an image into many segments where each segment or object is comprised of a group of spatially connected pixels. The DMP uses the morphological image operations opening and/or closing by reconstruction to identify pixel groups that contrast with their surrounding background by using structuring elements (or windows) of various sizes) using the observation angle and a height of the object included in metadata of the two observed images, and determine the collapsing direction based on a observation direction included in the metadata of the observed image (FIG. 6A. Paragraph [0092]-KLARIC discloses 6A depicts ingested imagery 610, which includes new imagery 612 and corresponding metadata 614 as well as previously stored imagery 616 and corresponding metadata 618. Further at paragraph [0092]-KLARIC discloses geometric-correction process of step 320 is to reference a set of information that describes elevation data, such as a Digital Elevation Model (DEM), that is associated with the geographic area. This data can be used along with other metadata, such as sensor-pointing geometry, to correct for geometric displacements in the pixels associated with the geographic area's topography (wherein the geometric displacements include differences in observation directions or angles between a first and second scene). Please also see paragraph [0119]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of CHOW in view of HU and in further view of AJADI and in further view of HAINLINE of having a non-transitory computer readable recording medium storing an image processing program which, when executed by a processor, performs: deforming object presence areas in two object presence images, in which one or more objects are present, with the teachings of KLARIC of having wherein the one or more processors are configured to further execute the software instructions to calculate the collapse amount using the observation angle and a height of the object included in metadata of the two observed images, and determine the collapsing direction based on a observation direction included in the metadata of the observed image.
Wherein having CHOW’s image processing device having wherein the one or more processors are configured to further execute the software instructions to calculate the collapse amount using the observation angle and a height of the object included in metadata of the two observed images, and determine the collapsing direction based on a observation direction included in the metadata of the observed image.
The motivation behind the modification would have been to obtain an image processing device that improves data robustness and allows for efficient and accurate display, since both CHOW and KLARIC identify and analyze regions in remote sensed imagery. Wherein CHOW’s system improves the robustness of data, while KLARIC efficiently and accurately displaying and querying high-resolution change detection imagery. Please see CHOW (US 10032077 B1), Abstract and Column [03], Line [22-35] and HAINLINE et al. (US 10553020 B1), Abstract and Paragraph [0180].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant`s disclosure.
Imai et al. (Imai et al., "A Method for Observing Seismic Ground Deformation from Airborne SAR Images”, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 1506-1509, doi: 10.1109/IGARSS.2019.8900352)-Observation of seismic ground deformation is one of the fundamental topics in remote sensing. A Synthetic Aperture Radar (SAR) has been used to obtain images representing geometrical properties of the ground surface. SAR images can be taken in nearly all weather conditions and in nearly all time. This paper proposes a ground deformation observation method using image correspondence matching, which employs phase-only correlation to estimate displacement between two SAR intensity images with sub-pixel accuracy. Through experiments using airborne SAR intensity images of the Kumamoto Earthquake, we demonstrate that the proposed method exhibits the efficient performance in observing seismic ground deformation....................Please see Fig. 1-4. Abstract.
Lebedev et al. (Lebedev et al. “CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 565–571, https://doi.org/10.5194/isprs-archives-XLII-2-565-2018, 2018)-We present a method for change detection in images using Conditional Adversarial Network approach. The original network architecture based on pix2pix is proposed and evaluated for difference map creation. The paper address three types of experiments: change detection in synthetic images without objects relative shift, change detection in synthetic images with small relative shift of objects, and change detection in real season-varying remote sensing images...................Please see Fig. 1 and 7-8. Abstract.
L. Bruzzone et al. (Bruzzone et al. "A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images," in Proceedings of the IEEE, vol. 101, no. 3, pp. 609-630, March 2013, doi: 10.1109/JPROC.2012.2197169)-This paper addresses change detection in multitemporal remote sensing images. After a review of the main techniques developed in remote sensing for the analysis of multitemporal data, the attention is focused on the challenging problem of change detection in very-high-resolution (VHR) multispectral images. In this context, we propose a framework that aims at defining a top-down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images. The proposed framework explicitly models the presence of different radiometric changes on the basis of the properties of multitemporal images, extracts the semantic meaning of radiometric changes, identifies changes of interest with strategies designed on the basis of the specific application, and takes advantage of the intrinsic multiscale/multilevel properties of the objects and the high spatial correlation between pixels in a neighborhood. This framework defines guidelines for the development of a new generation of change-detection methods that can properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data. In order to illustrate the use of the proposed framework, a real change-detection problem has been considered, which is described by a pair of VHR multispectral images acquired by the QuickBird satellite on the city of Trento, Italy. The proposed framework has been used for defining a system for change detection in the two images. Experimental results confirm the effectiveness of the developed system and the usefulness of the proposed framework.................... Please see Fig. 7, 9-10, 12-14. Abstract.
MURATA et al. (US 20150323666 A1)- The change detection device of the present invention inputs at least two SAR image sets each holding at least information indicative of a reflection intensity and a phase so as to be associated with each of pixels corresponding to a resolution cell within a field of vision for image capturing including a specific region. The information is generated from observation data formed of four basic polarization pairs, i.e. HH, HV, VH and VV polarization pairs observed by a synthetic aperture radar at generally the same time. The change detection device determines a polarization pair whose reflection intensity is not less than a predetermined value or the highest with respect to each of target pixels by using at least one SAR image set among input SAR image sets, and measures displacement at a spot corresponding to the target pixel based on a determined polarization pair and an input SAR image set................... Please see Fig. 1, 4 and 6. Abstract.
KENNEDY et al. (US 20200278452 A1)- A system and method for estimating the position of a receiver and/or timing information for a receiver based on emissions from radio signal sources meant for applications other than position/navigation/time (PNT) estimation.................. Please see Fig. 1 and 3. Abstract.
Rush et al. (US 9146312 B1)- Pre-processing is applied to a raw VideoSAR (or similar near-video rate) product to transform the image frame sequence into a product that resembles more closely the type of product for which conventional video codecs are designed, while sufficiently maintaining utility and visual quality of the product delivered by the codec....................Please see Fig. 1 and 4. Abstract.
Chartrand et al. (US 10852421 B1)-Sparse phase unwrapping is disclosed. A first image and a second image are received. The first image and the second image are coregistered. The first image and the second image comprise respective phase data. An unwrapped interferogram is generated, including by solving an optimization problem using a nonconvex penalty function, where minimizing the penalty function produces sparse minimizers...................Please see Fig. 10-12 and 17. Abstract.
Kabakian et al. (US 20210109210 A1)- Described is a stripmap SAR system on a vehicle comprising an antenna that is fixed and directed outward from the side of the vehicle, a SAR sensor, a storage, and a computing device. The computing device comprises a memory, one or more processing units, and a machine-readable medium on the memory. The machine-readable medium stores instructions that, when executed by the one or more processing units, cause the stripmap SAR system to perform various operations. The operations comprise: receiving stripmap range profile data associated with observed views of a scene; transforming the received stripmap range profile data into partial circular range profile data; comparing the partial circular range profile data to a template range profile data of the scene; and estimating registration parameters associated with the partial circular range profile data relative to the template range profile data to determine a deviation from the template range profile data................... Please see Fig. 1-6. Abstract.
KORB et al. (US 20150371431 A1)-A multi-temporal, multi-angle, automated target exploitation method is provided for processing a large number of images. The system geo-rectifies the images to a three-dimensional surface topography, co-registers groups of the images with fractional pixel accuracy, automates change detection, evaluates the significance of change between the images, and massively compresses imagery sets based on the statistical significance of change. The method improves the resolution, accuracy, and quality of information extracted beyond the capabilities of any single image, and creates registered six-dimensional image datasets appropriate for mathematical treatment using standard multi-variable analysis techniques from vector calculus and linear algebra such as time-series analysis and eigenvector decomposition................... Please see Fig. 2-5. Abstract.
BRUZZONE et al. (EP 3896482 A1)-The present invention describes a method for the computer-implemented generation of a synthetic data set for training a convolutional neural network for an Interferometric Synthetic Aperture Radar. In a first step, a number of interferometric phase images being generated from a respective digital elevation model (DEM) is retrieved, where each digital elevation model (DEM) is back-geocoded from an image taken with a SAR system using a known acquisition geometry. In a second step, a number of amplitude images and coherence images is generated by providing a set of different patterns derived from ramp profiles and from natural patterns and by mapping the natural pattern digital values into a scale used for the ramp profile digital values. In a last step, a number of noisy images is repeatedly generated by grouping one of the phase images, one of the amplitude images and one of the coherence images into a triple and combining each triple to form a respective noisy image, wherein each of the noisy images is assigned to one of a plurality of different categories................... Please see Fig. 2 and 4-5. Abstract.
Ulander et al. (US 6466156 B1)- A method of detecting, by means of a SAR radar, objects that change with time within a ground area. The SAR radar is supported by a platform in essentially rectilinear motion during a synthetic aperture and the ground area is reproduced at least twice in succession from different synthetic apertures. A two-dimensional SAR image is generated from each synthetic aperture. The SAR images are matched with each other by a method in which each image position in one image is associated with the same ground area in the other image, the images being filtered, knowing location data for the antennae and based on the fact that the cylinder geometry of the SAR images is projected onto the ground surface, so that only common spectral components of the reflectivity of the ground are extracted and used in the matching................... Please see Fig. 1-2, 4. Abstract.
Pennings et al. (US 20200258296 A1)- Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.................. Please see Fig. 1-2, 4, 7 and 11. Abstract.
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/AARON TIMOTHY BONANSINGA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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