# US 2025/0095343 A1 · Image processing apparatus, image processing method, and non-transitory computer-readable storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095343 A1 - Document ID: US20250095343A1 - Status: Published application - Kind: A1 - Application number: 18/830,755 - Filed: 2024-09-11 - Published: 2025-03-20 - Earliest claimed: 2023-09-19 - Assignees: Canon Kabushiki Kaisha - Applicants: Canon Kabushiki Kaisha - Inventors: Yoshiki OHNO - Web page: https://patentgrep.com/patent/US20250095343A1 ## CPC - G06V 10/778: Active pattern-learning, e.g. online learning of image or video features - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/776: Validation; Performance evaluation - G06V 10/82: using neural networks - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An image processing apparatus comprises a calculation unit configured to perform an error calculation to obtain an error between a calculation result of a learning model to which a learning image is input and a teacher image corresponding to the learning image, and a learning unit configured to perform learning of the learning model based on the error. The calculation unit performs the error calculation by making an error corresponding to a boundary region of an object in the teacher image larger than other regions. ## Claims ### Claim 1 An image processing apparatus comprising: a calculation unit configured to perform an error calculation to obtain an error between a calculation result of a learning model to which a learning image is input and a teacher image corresponding to the learning image; and a learning unit configured to perform learning of the learning model based on the error; wherein the calculation unit performs the error calculation by making an error corresponding to a boundary region of an object in the teacher image larger than other regions. ### Claim 2 (depends on 1) The image processing apparatus according to claim 1, wherein the calculation unit obtains a sum in which an error for each pixel between the calculation result and the teacher image is weighted as an error between the calculation result and the teacher image, a weight for an error between a pixel belonging to the boundary region in the teacher image and a corresponding pixel in the calculation result being larger than a weight for an error between a pixel not belonging to the boundary region in the teacher image and a corresponding pixel in the calculation result. ### Claim 3 (depends on 2) The image processing apparatus according to claim 2, wherein the calculation unit sets a value corresponding to an area ratio between a boundary region and a non-boundary region in the teacher image as a weight for an error between a pixel belonging to the boundary region in the teacher image and a corresponding pixel in the calculation result. ### Claim 4 (depends on 1) The image processing apparatus according to claim 1, wherein the learning unit learns the learning model by updating a parameter of the learning model so as to further reduce an error between the calculation result and the teacher image. ### Claim 5 (depends on 1) The image processing apparatus according to claim 1, wherein the boundary region in the teacher image is a region of a boundary line between regions having different assigned labels. ### Claim 6 (depends on 1) The image processing apparatus according to claim 1, wherein the boundary region in the teacher image is a region including a boundary line between regions having different assigned labels and a peripheral region of the boundary line. ### Claim 7 (depends on 1) The image processing apparatus according to claim 1, wherein the learning unit dynamically changes a width of the boundary region. ### Claim 8 (depends on 1) The image processing apparatus according to claim 1, wherein the calculation unit performs the error calculation by emphasizing an error corresponding to a boundary region in a binarized teacher image obtained by binarizing the teacher image. ### Claim 9 An image processing apparatus comprising: an input unit configured to input an image; and a detection unit configured to detect an object from the input image by using a parameter of a learning model learned by making an error corresponding to a boundary region of the object included in the image larger than other regions. ### Claim 10 An image processing method performed by an image processing apparatus, comprising: performing an error calculation to obtain an error between a calculation result of a learning model to which a learning image is input and a teacher image corresponding to the learning image; performing learning of the learning model based on the error, and performing, in the calculation, the error calculation by making an error corresponding to a boundary region of an object in the teacher image larger than other regions. ### Claim 11 An image processing method comprising: inputting an image; and detecting the object from the input image using a parameter of a learning model learned by making an error corresponding to a boundary region of an object included in the image larger than other regions. ### Claim 12 A non-transitory computer-readable storage medium storing a computer program for causing a computer to function as: a calculation unit configured to perform an error calculation to obtain an error between a calculation result of a learning model to which a learning image is input and a teacher image corresponding to the learning image; and a learning unit configured to perform learning of the learning model based on the error; wherein the calculation unit performs the error calculation by making an error corresponding to a boundary region of an object in the teacher image larger than other regions. ### Claim 13 A non-transitory computer-readable storage medium storing a computer program for causing a computer to function as: an input unit configured to input an image; and a detection unit configured to detect an object from the input image by using a parameter of a learning model learned by making an error corresponding to a boundary region of the object included in the image larger than other regions.