# US 10,319,096 B2 · Automated tattoo recognition techniques > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,319,096 B2 - Document ID: US10319096B2 - Status: Granted - Kind: B2 - Application number: 16/223,816 - Filed: 2018-12-18 - Published: 2019-04-25 - Granted: 2019-06-11 - Earliest claimed: 2016-09-12 - Assignees: MorphoTrak, LLC - Applicants: MorphoTrak, LLC - Inventors: Darrell Hougen; Peter Zhen-Ping Lo - Web page: https://patentgrep.com/patent/US10319096B2 ## CPC - G06V 10/764: using classification, e.g. of video objects - G06F 18/2413: based on distances to training or reference patterns - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06T 7/337: {involving reference images or patches} - G06T 7/35: using statistical methods - G06T 7/41: based on statistical description of texture - G06T 7/73: using feature-based methods - G06T 7/90: Determination of colour characteristics - G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes} - 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 - G06V 20/60: Type of objects - G06V 40/10: Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands - G06V 40/193: {Preprocessing; Feature extraction} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo. ## Claims ### Claim 1 A method performed by one or more processors, the method comprising: obtaining, by the one or more processors, an image that likely includes a tattoo; determining, by the one or more processors, a set of one or more segmentation parameters for the image; identifying, by the one or more processors, a plurality of blocks associated with the tattoo based on the determined set of one or more segmentation parameters for the image, wherein each block corresponds a particular region of the image that (i) likely includes the tattoo and (ii) comprises an overlapping portion included in one or more adjacent blocks; computing, by the one or more processors, a set of feature statistics for each block included in the plurality of blocks; generating, by the one or more processors, a segmentation mask for the image based on the set of feature statistics computed for the plurality of blocks; and providing, by the one or more processors, the segmentation mask for output. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of blocks are arranged in the image as a two-dimensional array of overlapping blocks. ### Claim 3 (depends on 2) The method of claim 2, further comprising computing a confidence score for each of the plurality of blocks, the confidence scores representing a respective likelihood that at least a portion of a block includes a predetermined graphical attribute associated with a tattoo. ### Claim 4 (depends on 3) The method of claim 3, further comprising: identifying a subset of the plurality of blocks that have confidence scores satisfying a predetermined threshold; and wherein the segmentation mask for the image is generated based on the identified subset of the plurality of blocks. ### Claim 5 (depends on 3) The method of claim 3, wherein: the segmentation mask comprises a binary image; and pixel values of pixels included in the binary image are determined based on the confidence scores computed for the plurality of blocks. ### Claim 6 (depends on 1) The method of claim 1, wherein generating the segmentation mask for the image comprises: grouping the plurality of blocks based on the feature statistics computed for the plurality of blocks; and identifying one or more regions that each include blocks that have been grouped based on the feature statistics computed for the plurality of blocks. ### Claim 7 (depends on 1) The method of claim 1, wherein the set of feature statistics computed for each block included in the plurality of blocks comprises an order statistic that indicates a median value for a characteristic for the plurality of blocks. ### Claim 8 A system comprising: one or more computers; and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: obtaining, by the one or more processors, an image that likely includes a tattoo; determining, by the one or more processors, a set of one or more segmentation parameters for the image; identifying, by the one or more processors, a plurality of blocks associated with the tattoo based on the determined set of one or more segmentation parameters for the image, wherein each block corresponds a particular region of the image that (i) likely includes the tattoo and (ii) comprises an overlapping portion included in one or more adjacent blocks; computing, by the one or more processors, a set of feature statistics for each block included in the plurality of blocks; generating, by the one or more processors, a segmentation mask for the image based on the set of feature statistics computed for the plurality of blocks; and providing, by the one or more processors, the segmentation mask for output. ### Claim 9 (depends on 8) The system of claim 8, wherein the plurality of blocks are arranged in the image as a two-dimensional array of overlapping blocks. ### Claim 10 (depends on 9) The system of claim 9, wherein the operations further comprise computing a confidence score for each of the plurality of blocks, the confidence scores representing a respective likelihood that at least a portion of a block includes a predetermined graphical attribute associated with a tattoo. ### Claim 11 (depends on 10) The system of claim 10, wherein the operations further comprise: identifying a subset of the plurality of blocks that have confidence scores satisfying a predetermined threshold; and wherein the segmentation mask for the image is generated based on the identified subset of the plurality of blocks. ### Claim 12 (depends on 10) The system of claim 10, wherein: the segmentation mask comprises a binary image; and pixel values of pixels included in the binary image are determined based on the confidence scores computed for the plurality of blocks. ### Claim 13 (depends on 8) The system of claim 8, wherein generating the segmentation mask for the image comprises: grouping the plurality of blocks based on the feature statistics computed for the plurality of blocks; and identifying one or more regions that each include blocks that have been grouped based on the feature statistics computed for the plurality of blocks. ### Claim 14 (depends on 8) The system of claim 8, wherein the set of feature statistics computed for each block included in the plurality of blocks comprises an order statistic that indicates a median value for a characteristic for the plurality of blocks. ### Claim 15 A non-transitory computer-readable storage device encoded with computer program instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining, by the one or more processors, an image that likely includes a tattoo; determining, by the one or more processors, a set of one or more segmentation parameters for the image; identifying, by the one or more processors, a plurality of blocks associated with the tattoo based on the determined set of one or more segmentation parameters for the image, wherein each block corresponds a particular region of the image that (i) likely includes the tattoo and (ii) comprises an overlapping portion included in one or more adjacent blocks; computing, by the one or more processors, a set of feature statistics for each block included in the plurality of blocks; generating, by the one or more processors, a segmentation mask for the image based on the set of feature statistics computed for the plurality of blocks; and providing, by the one or more processors, the segmentation mask for output. ### Claim 16 (depends on 15) The device of claim 15, wherein the plurality of blocks are arranged in the image as a two-dimensional array of overlapping blocks. ### Claim 17 (depends on 16) The device of claim 16, wherein the operations further comprise computing a confidence score for each of the plurality of blocks, the confidence scores representing a respective likelihood that at least a portion of a block includes a predetermined graphical attribute associated with a tattoo. ### Claim 18 (depends on 17) The device of claim 17, wherein the operations further comprise: identifying a subset of the plurality of blocks that have confidence scores satisfying a predetermined threshold; and wherein the segmentation mask for the image is generated based on the identified subset of the plurality of blocks. ### Claim 19 (depends on 17) The device of claim 17, wherein: the segmentation mask comprises a binary image; and pixel values of pixels included in the binary image are determined based on the confidence scores computed for the plurality of blocks. ### Claim 20 (depends on 15) The device of claim 15, wherein generating the segmentation mask for the image comprises: grouping the plurality of blocks based on the feature statistics computed for the plurality of blocks; and identifying one or more regions that each include blocks that have been grouped based on the feature statistics computed for the plurality of blocks.