Geometric Pattern Matching via Key Contour Points
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Solution Overview
Problem
Conventional geometric pattern matching technologies are inefficient due to the need for comparing all positions of contour pixels, leading to long processing times and limitations in noise and occlusion handling.
Innovation Solution
A method using a geometric pattern matching device that determines reference and detected contour points by removing noise and applying conditional probability-based matching, allowing for rapid and accurate detection of patterns robust to noise and occlusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all positions of contour pixels are compared in conventional geometric pattern matching, then comprehensive pattern detection is achieved, but processing time becomes excessively long
Solution Approach 1:
The patent segments the contour pixels into key contour points that satisfy specific geometric conditions (such as extreme points, inflection points, or points with specific curvature properties). Instead of comparing all contour pixels, the method selectively compares only these key points, dramatically reducing the number of comparisons while maintaining pattern detection accuracy.
Solution Approach 2:
The patent applies different treatment to different parts of the contour by identifying key points with specific local geometric properties. These key points are selected based on local curvature, position, or other geometric characteristics, making the comparison process more efficient by focusing on locally significant features rather than uniformly processing all points.
2Measurement precision
If conventional geometric pattern matching compares absolute positions of contour pixels, then exact geometric matching is achieved, but the method becomes highly sensitive to noise and occlusion
Solution Approach 1:
The patent transforms the comparison from absolute position matching to relative geometric relationship matching. By comparing geometric relationships (such as distances, angles, or relative positions between key contour points) rather than absolute coordinates, the method becomes invariant to translation and rotation, thereby improving robustness to noise and occlusion while maintaining geometric matching accuracy.
3Adaptability or versatility
If geometric patterns are extracted from randomly selected objects, then general pattern recognition is achieved, but the matching process becomes computationally expensive
Solution Approach 1:
The patent performs preliminary extraction and storage of key contour points and their geometric relationships during the learning phase. By pre-processing and storing only the essential geometric features rather than complete contour data, the method accelerates the matching process while maintaining the ability to recognize various patterns.
Solution Approach 2:
The patent extracts only the essential geometric features (key contour points and their relationships) from the complete object data. This extraction of critical information eliminates redundant data, reducing computational complexity during matching while preserving the core geometric characteristics needed for pattern recognition.
Data Source
AI summary
A geometric pattern matching method and a device for performing the method includes determining, by a geometric pattern matching device, information on reference geometric pattern contour points for a learning object on a learning image. The method can further include determining, by the geometric pattern matching device, information on detection object contour points for a detection object on a detection image, and performing, by the geometric pattern matching device, geometric pattern matching between the learning object and the detection object on the basis of the information on the reference geometric pattern contour points and the information on the detection object contour points.