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
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8-10, 11-12, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 2020/0151888) in view of Zhang (US 2021/0116491).
As per claims 1 and 11, Chen teaches, an image analysis method and apparatus applied to an image analysis apparatus with an operation processor (Chen, ¶[0010] “the extracted images are processed” therefore there is a processor), the image analysis method comprising the operation processor receiving a surveillance image acquired by an image receiver (Chen, ¶[0010] “In the intelligent method for viewing the surveillance videos of the present invention , the images of the videos from the plurality of channels extracted at the same time point are combined into a group of images, and the visual interest detection is performed on each group of images to obtain the visual image of interest corresponding to the group” this represents receiving a surveillance image acquired by an image receiver); the operation processor transforming the surveillance image into a first low frequency image ( Chen, fig.3 S1042 “for low-frequency component”) and plural first high frequency images via wavelet transform (Chen, fig.3 S1043 “for the high frequency components…” ); the operation processor transforming the first low frequency image into a second low frequency image and plural second high frequency images via another wavelet transform (Chen fig.3 S1042 and S1043 going into S1044 to perform wavelet transform); second high frequency group data generated by the plural second high frequency images and low frequency group data generated by the second low frequency image for acquiring feature integration data (Chen, fig.3 S1044 represents feature integration data since the entire thing gets stitched together and then that would highlight the features and see fig.2 S1033-34 visual frame of interest visualization region).
Chen doesn’t clearly teach, the operation processor applying down sampling process to first high frequency group data generated by the plural first high frequency images (; and the operation processor applying depth integration to the first high frequency group data after the down sampling process.
However, Zhang teaches, the operation processor applying down sampling process to first high frequency group data generated by the plural first high frequency images (Chen, ¶[0024] “In the second step, the interval region between the high-frequency component regions is down-sampled or interpolated to obtain a low-frequency component region.” This represents down sampling process to first high frequency group data ); and the operation processor applying depth integration to the first high frequency group data after the down sampling process (Chen, ¶ [0052] “FIG. 7 is a schematic diagram of an optimal depth neural network location and recognition model of another embodiment of the present invention. The method will be described below with reference to FIG. 7.” This represents applying depth integration by having a depth neural network).
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 Zhang with Chen’s abilities to be able to down-sample the high-frequency component regions.
The motivation would have been to improve system efficiency.
As per claims 2 and 12, Chen in view of Zhang teaches, the image analysis method of claim 1, further comprising: the operation processor applying at least one of standardization process, discrete cosine transform process, frequency domain feature extraction process and expansion process to the second low frequency image for acquiring the low frequency group data (Zhang, ¶[0027] “The sliding window Fourier transform is to slide the entire waveform by the length of one window, and each time the data in the window is subjected to discrete Fourier transform.” This represents frequency domain feature extraction process ).
As per claims 8 and 18, Chen in view of Zhang teaches, the image analysis method of claim 1, wherein the operation processor applies fully connected process to the feature integration data for generating a classification result of the surveillance image (Chen, fig.3 S1045 visual images stitched together would represent feature integration data for generating classification results to be able to stitch the images together ).
As per claims 9 and 19, Chen in view of Zhang teaches, the image analysis method of claim 8, further comprising: the operation processor applying another down sampling process to the feature integration data; and the operation processor applying convolution process and the depth integration to the feature integration data after the another down sampling process for acquiring another feature integration data (Zhang, ¶ [0030] “In the present disclosure, a cubic spline interpolation method can be used to perform down-sampling processing or interpolation processing to capture waveform segment.” And [0010] FIGS. 4a-4c are schematic structural diagrams of a convolution block of the present invention according to the present invention.); wherein the fully connected process is applied to the another feature integration data for generating the classification result (Chen, fig.3 S1045 represents another feature integration).
As per claims 10 and 20, Chen in view of Zhang teaches, the image analysis method of claim 9, wherein parameter setting of the down sampling process is the same as or different from parameter setting of the another down sampling process (Zhang, ¶[0024] “the interval region between the high-frequency component regions is down-sampled or interpolated to obtain a low-frequency component region.” This would represent the down sampling process is the same as or different from parameter setting of the another down sampling process, and see ¶[0021] In the following, the waveform preprocessing and waveform splicing, the deep neural network location identification model and hyper-parameter machine training of the present disclosure are further explained. ).
Allowable Subject Matter
Claims 3-7, and 13-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The limitations “wherein the plural first high frequency images comprise a first horizontal difference high frequency image, a first vertical difference high frequency image and a first oblique angle difference high frequency image transformed from the surveillance image via the wavelet transform, the plural second high frequency images comprise a second horizontal difference high frequency image, a second vertical difference high frequency image and a second oblique angle difference high frequency image transformed from the first low frequency image via the wavelet transform.” Were not found in the prior art. Further it is asked that “Another wavelet transform” Is further differentiated from “a wavelet transform”.
Close prior art:
Chen (US 2020/0154046) ¶[0032] In step S1044, inverse wavelet transform is performed on the fused low-frequency portion and the high-frequency portion to obtain the final stitched image of the images A and B; [0034] It should be noted that, in the prior art, when image fusion is performed using wavelet transformation, image fusion is performed on each decomposition layer respectively. However, for low frequency portion, when the number of decomposition layers is low, the image contrast will degrade. In contrast, in the present embodiment, a visual interest based fusion algorithm is adopted for the low-frequency components, which can fuse the details of the source images together with a better fusion effect and not elaborated herein. [0035] In addition, the virtual surveillance scene processing module 106 in this embodiment is mainly configured for converting all the panoramic images into a corresponding virtual surveillance scene. In a specific implementation, a panoramic image is a basic unit constituting a virtual surveillance scene. In the prior art, the corresponding virtual surveillance scene may be converted using a cube mode, a spherical mode, and a cylindrical mode. In this embodiment, the cylindrical mode is used, that is, the panoramic images are stitched into a cylindrical shape centered on the camera viewpoint, so that a 360-degree ring-view of the line of sight can be achieved in the horizontal direction, and the rotation angle of the line of sight in the vertical direction is less than 180 degrees, which is not elaborated herein.
Lee (US 2017/0200044) Abstract “An apparatus and a method for providing a surveillance image based on a depth image. The apparatus according to the present invention includes: a first camera capturing a depth image for a subject in a predetermined capturing region; a second camera capturing a color image for the subject in the predetermined capturing region; a subject identification unit identifying a subject in the capturing region; an image processing unit extracting the color image corresponding to a region of the identified subject from the color image and synthesizing the extracted color image with a position corresponding to the depth image to generate a synthesized image; and a control unit providing the depth image when the subject is not identified by the subject identification unit and providing the synthesized image only when the subject is identified by the subject identification unit.”
Hunag (US 2015/0078444) “Method for coding or recognizing of surveillance videos is provided to improve compressing efficiency and recognizing accuracy of surveillance videos. The method for coding surveillance videos includes: establishing a background database and a foreground object database; wherein, the background database includes a set of background images; the foreground object database includes a set of foreground objects; coding a surveillance image by referring to a background image in the background database and a foreground object in the foreground object database.”
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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