# US 12,008,767 B2 · Systems and methods for image segmentation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,767 B2 - Document ID: US12008767B2 - Status: Granted - Kind: B2 - Application number: 17/358,003 - Filed: 2021-06-25 - Published: 2021-10-21 - Granted: 2024-06-11 - Earliest claimed: 2018-12-25 - Assignees: Zhejiang Dahua Technology Co., Ltd. - Applicants: Zhejiang Dahua Technology Co., Ltd. - Inventors: Feng Wang; Lijian Mao; Haitao Sun; Jianping Xiong - Web page: https://patentgrep.com/patent/US12008767B2 ## CPC - G06T 7/194: involving foreground-background segmentation - G06T 7/11: Region-based segmentation - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/50: by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis - G06T 2207/20021: Dividing image into blocks, subimages or windows - G06T 2207/20052: Discrete cosine transform [DCT] - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract The present disclosure relates to systems and methods for image segmentation. The system may include at least one processor that is directed to obtain an image; divide the image into a plurality of image blocks; determine, for each of the plurality of image blocks, a compressed representation of one or more features of the image block; group the plurality of image blocks into at least two different categories based on the plurality of compressed representations; and extract a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks. ## Claims ### Claim 1 A system for image segmentation, comprising: a storage medium storing a set of instructions; and at least one processor in communication with the storage medium, wherein when executing the set of instructions, the at least one processor is directed to: obtain an image; divide the image into a plurality of image blocks; determine, for each of the plurality of image blocks, a compressed representation of one or more features of the image block; group the plurality of image blocks into at least two different categories based on the plurality of compressed representations; and extract a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks, wherein for each of the plurality of image blocks, the compressed representation of the one or more features of the image block includes a set of binary codes; and the plurality of image blocks are grouped based on a bit distribution in the set of binary codes. ### Claim 2 (depends on 1) The system of claim 1, wherein the set of binary codes are generated according to a perceptual hash algorithm. ### Claim 3 (depends on 1) The system of claim 1, wherein for each of the plurality of image blocks, the one or more features of the image block include a texture feature of the image block. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least two different categories include a category associated with a foreground of the image and a category associated with a background of the image. ### Claim 5 (depends on 1) The system of claim 1, wherein to extract a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks, the at least one processor is directed to: determine, among the at least one of the at least two different categories of image blocks, one or more target image blocks based on one or more neighboring image blocks thereof; and extract the region of interest from the image based on the one or more target image blocks. ### Claim 6 (depends on 5) The system of claim 5, wherein to extract the region of interest from the image based on the one or more target image block, the at least one processor is directed to: merge the one or more target image blocks that belong to one of the at least two different categories to form the region of interest. ### Claim 7 (depends on 5) The system of claim 5, wherein to extract the region of interest from the image based on the one or more target image blocks, the at least one processor is directed to: connect two or more of the one or more target image blocks; and extract the region of interest from the image based on the connected two or more of the one or more target image blocks. ### Claim 8 (depends on 5) The system of claim 5, wherein to determine, among at least one of the at least two different categories of image blocks, one or more target image blocks based on one or more neighboring image blocks thereof, the at least one processor is directed to: determine a candidate image block and its one or more neighboring image blocks; determine, for each of the one or more neighboring image blocks of the candidate image block, a category to which the neighboring image block belongs; and designate the candidate image block as a target image block in response to that the one or more neighboring image blocks of the candidate image block belong to a same category. ### Claim 9 (depends on 1) The system of claim 1, wherein the at least one processor is further directed to: optimize the extracted region of interest. ### Claim 10 A method for image segmentation, implemented on a computing device including at least one processor and at least one storage medium, the method comprising: obtaining an image; dividing the image into a plurality of image blocks; determining, for each of the plurality of image blocks, a compressed representation of one or more features of the image block; grouping the plurality of image blocks into at least two different categories based on the plurality of compressed representations; and extracting a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks, wherein for each of the plurality of image blocks, the compressed representation of the one or more features of the image block includes a set of binary codes; and the plurality of image blocks are grouped based on a bit distribution in the set of binary codes. ### Claim 11 (depends on 10) The method of claim 10, wherein extracting a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks, including: determining, among at least one of the at least two different categories of image blocks, one or more target image blocks based on one or more neighboring image blocks thereof; and extracting the region of interest from the image based on the one or more target image blocks. ### Claim 12 (depends on 11) The method of claim 11, wherein extracting the region of interest from the image based on the one or more target image block, including: merging the one or more target image blocks that belong to one of the at least two different categories to form the region of interest. ### Claim 13 (depends on 11) The method of claim 11, wherein extracting the region of interest from the image based on the one or more target image blocks, including: connecting two or more of the one or more target image blocks; and extracting the region of interest from the image based on the connected two or more of the one or more target image blocks. ### Claim 14 (depends on 11) The method of claim 11, wherein determining, among at least one of the at least two different categories of image blocks, one or more target image blocks based on one or more neighboring image blocks thereof, including: determining a candidate image block and its one or more neighboring image blocks; determining, for each of the one or more neighboring image blocks of the candidate image block, a category to which the neighboring image block belongs; and designating the candidate image block as a target image block in response to that the one or more neighboring image blocks of the candidate image block belong to a same category. ### Claim 15 (depends on 10) The method of claim 10, further including: optimizing the extracted region of interest. ### Claim 16 A non-transitory computer readable medium, comprising a set of instructions for image segmentation, wherein when executed by at least one processor, the set of instructions directs the at least one processor to: obtain an image; divide the image into a plurality of image blocks; determine, for each of the plurality of image blocks, a compressed representation of one or more features of the image block; group the plurality of image blocks into at least two different categories based on the plurality of compressed representations; and extract a region of interest from the image based on at least one of the at least two different categories of the plurality of image blocks, wherein for each of the plurality of image blocks, the compressed representation of the one or more features of the image block includes a set of binary codes; and the plurality of image blocks are grouped based on a bit distribution in the set of binary codes.