Hough Space Pattern Recognition for False Detection Suppression
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Solution Overview
Problem
Conventional recognition systems using Hough transform face issues with false pattern detection around true patterns due to noise, leading to increased processing requirements and inefficiencies.
Innovation Solution
A recognition system with a Hough space designed to express similar patterns as closer points, allowing for local processing to suppress false pattern appearance by defining an inter-specific-pattern distance that matches the distance in the Hough space, enabling effective detection of specific patterns while reducing noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Hough transform is used for pattern detection, then pattern recognition capability is achieved, but false patterns appear around true patterns due to noise
Solution Approach 1:
The patent applies local quality by performing localized processing in the Hough space. Specifically, it identifies regions around detected peak points and applies suppression only to those local regions where false patterns are likely to occur, rather than processing the entire Hough space uniformly. This localized approach suppresses false patterns while preserving true patterns effectively.
Solution Approach 2:
The patent changes parameters in the Hough space by introducing a suppression mechanism that modifies the vote values or weights in specific regions. By adjusting these parameters locally around peak points, the system reduces the influence of noise-induced false patterns while maintaining the detection accuracy for true patterns.
2Reliability
If conventional techniques suppress false patterns by removing influence from associated regions, then false pattern detection is reduced, but processing amount increases significantly
Solution Approach 1:
The patent significantly improves processing efficiency by applying suppression only to local regions around detected peak points in the Hough space, rather than processing the entire space. This localized approach reduces the computational burden while maintaining effective false pattern suppression, directly addressing the productivity concern.
Solution Approach 2:
The patent applies partial action by performing suppression only where necessary - specifically in regions around peak points where false patterns are most likely to occur. This partial processing approach avoids the excessive computation that would result from processing the entire Hough space, thereby improving processing efficiency while maintaining suppression effectiveness.
Data Source
AI summary
A recognition system of this invention has feature point detection means (120), Hough transform means (130), and specific pattern output means (140). In the Hough transform means (130), a Hough space is designed so that a magnitude relation of a distance between points in the Hough space is equivalent to a predetermined magnitude relation of an inter-specific-pattern distance indicative of a difference between specific patterns. The recognition system detects the specific patterns using the Hough space. By adopting such a structure to express more similar specific patterns in an image as closer points also in the Hough space, it is possible to achieve an object of this invention.