Spatial Gradient Map for Person Segmentation
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Solution Overview
Problem
Existing image segmentation methods face challenges in accurately separating individuals from backgrounds due to variations in poses and dress, leading to incorrect segmentation of pixels based on similar color values, especially when the background has a similar color to the person's attire.
Innovation Solution
A spatial gradient map is generated based on detected anatomical points of a person in an image, forming a frame that extends outward to indicate the likelihood of pixel membership, enhancing the accuracy of person segmentation by excluding or including pixels based on their distance from the frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color-based segmentation is used to separate person from background, then segmentation speed is maintained, but segmentation accuracy deteriorates when background and person attire have similar colors
Solution Approach 1:
The patent segments the segmentation process into multiple components: detecting anatomical points, generating a frame, creating a spatial gradient map, and finally performing pixel classification. This multi-stage segmentation approach improves accuracy by incorporating spatial context while maintaining manageable computational complexity through modular processing steps.
Solution Approach 2:
The patent introduces a spatial gradient map as an intermediary data structure between the detected anatomical points and the final segmentation result. This gradient map serves as a mediator that encodes spatial relationships and guides pixel classification, resolving the contradiction by adding an intermediate processing layer that improves accuracy without excessive complexity.
2Measurement precision
If spatial gradient map is generated to improve segmentation accuracy, then segmentation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by generating the spatial gradient map only in regions relevant to the person's body, rather than processing the entire image uniformly. The gradient map is constructed based on detected anatomical points and frame, concentrating computational resources where they are most needed for accurate segmentation while reducing overall computational power requirements.
3Reliability
If anatomical points detection is performed to generate accurate frame, then segmentation reliability improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by detecting anatomical points and generating the spatial gradient map before the actual pixel classification step. This preliminary processing establishes a reliable spatial framework that guides subsequent segmentation, improving overall reliability while reducing the computational burden during the final classification phase.
Data Source
AI summary
In one example in accordance with the present disclosure, a system is described. The system includes a pose estimator to identify a plurality of anatomical points on a person depicted in an image. A frame former of the system generates a frame for the person by connecting a first set of the plurality of anatomical points to form a skeleton and forming at least a head region and a torso region of the person based on a second set of the plurality of anatomical points. A map generator of the system generates a spatial gradient map projecting outwards from the frame. In the system, the spatial gradient map is based on pixel distance from the frame and an intensity along the gradient map indicates a pixels likelihood of form a part of the person.