Hand Segmentation Using Wrist Line Detection in 3D Images
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Solution Overview
Problem
Existing hand segmentation systems in computer vision are not robust due to their reliance on assuming the palm of the hand is the largest circular distention, leading to incorrect segmentation when faced with varying hand shapes and orientations.
Innovation Solution
The system estimates an optimal dividing line between the hand and forearm based on the narrowest region of the wrist area, using a wrist line detection method that considers the general wrist location and forearm orientation, allowing for accurate segmentation regardless of hand orientation or shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hand segmentation is performed by identifying the largest circular distention in the image, then the segmentation process is simple, but the segmentation accuracy deteriorates when hand shapes and orientations vary
Solution Approach 1:
The patent segments the hand detection process into multiple components: detecting the forearm, identifying candidate wrist lines, evaluating wrist line candidates based on multiple criteria, and selecting the optimal wrist line. This multi-stage segmentation approach replaces the single-step circular distention method, improving accuracy while maintaining manageable complexity through systematic breakdown of the problem
Solution Approach 2:
The patent changes the detection parameter from simply finding the largest circular distention to evaluating multiple wrist line candidates based on multiple parameters including distance from forearm, orientation relative to forearm, and anatomical plausibility. This parameter expansion allows the system to adapt to various hand shapes and orientations, resolving the accuracy issue
2Productivity
If hand segmentation assumes the palm is the largest circular region, then the method is computationally efficient, but it produces incorrect segmentation for non-standard hand orientations
Solution Approach 1:
The patent performs preliminary detection of the forearm and establishes a reference frame before evaluating wrist line candidates. By pre-identifying the forearm region and establishing orientation references, the system prepares the groundwork for accurate wrist line detection, enabling efficient processing while maintaining correctness across different hand orientations
Solution Approach 2:
The patent creates a universal wrist line detection method that works across multiple hand orientations and shapes by evaluating candidates based on geometric relationships rather than assuming a fixed palm position. The multi-functional evaluation criteria can adapt to various hand configurations, ensuring both efficiency and correctness
Data Source
AI summary
Techniques are provided for segmentation of a hand from a forearm in an image frame. A methodology implementing the techniques according to an embodiment includes estimating a wrist line within an image shape that includes a forearm and a hand. The wrist line estimation is based on a search for a minimum width region of the shape that is surrounded by adjacent regions of greater width on each side of the minimum width region. The method also includes determining a forearm segment, and a hand segment that is separated from the forearm segment by the wrist line. The method further includes labeling the forearm segment and the hand segment. The labeling is based on a connected component analysis of the forearm segment and the hand segment. The method further includes removing the labeled forearm segment from the image frame to generate the image segmentation of the hand.