Shape Recognition Device Dual Feature Point Extraction
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Solution Overview
Problem
Current shape recognition technologies lack precision in identifying external shapes of objects, particularly anatomical features like fingers, palms, and arms, due to limited feature point extraction methods.
Innovation Solution
A shape recognition device and method that includes an external-shape detecting unit, a first extracting unit for feature points, and a second extracting unit to identify additional feature points, enabling more precise recognition by generating polygons and calculating feature point directions, which facilitates the detection of anatomical features like fingers, palms, and arms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only a single feature point extraction method is used, then the device complexity is low, but the measurement precision of external shape recognition is insufficient
Solution Approach 1:
The feature point extraction process is segmented into two distinct units: a first extracting unit that identifies primary feature points (such as vertices of the external shape), and a second extracting unit that identifies secondary feature points (such as points on edges or surfaces). This segmentation allows each unit to specialize in extracting different types of geometric information, thereby improving overall measurement precision without requiring a single overly complex extraction algorithm
Solution Approach 2:
The shape recognition device is designed with multi-functional capability by integrating two different feature point extraction methods within a single system. The first extracting unit handles vertex-based features while the second extracting unit handles edge/surface-based features, allowing the system to universally recognize various types of geometric characteristics across different objects and shape types
2Measurement precision
If multiple feature points are extracted using different methods, then the recognition precision of anatomical features is improved, but the device complexity increases
Solution Approach 1:
Different extraction methods are applied to different local regions of the external shape based on their geometric characteristics. The first extracting unit focuses on vertex regions where angular features are prominent, while the second extracting unit focuses on edge and surface regions where linear or curved features are more significant. This local quality approach ensures that each region is analyzed using the most appropriate method, improving anatomical feature recognition precision
Solution Approach 2:
The extraction system is divided into specialized sub-units that process different aspects of anatomical features. The first extracting unit extracts key landmark points such as finger tips and joint positions, while the second extracting unit extracts additional feature points along the contours and surfaces of anatomical structures. This segmentation enables precise recognition of complex anatomical features by combining multiple types of geometric information
Data Source
AI summary
A shape recognition device, a shape recognition program, and a shape recognition method that can obtain more precise information for recognizing an external shape of an object are provided. A shape recognition device of the present invention includes an external-shape detecting unit that detects an external shape of an object, a first extracting unit that extracts a first feature point on the external shape based on the external shape detected by the external-shape detecting unit, and a second extracting unit that extracts a second feature point that exists on the external shape or in the external shape and differs from the first feature point. As a result, more precise information for recognizing the external shape of the object can be obtained.