Intelligent character defect detection algorithm based on edge shape matching

Abstract

The invention discloses a character defect intelligent detection algorithm based on edge shape matching. The algorithm comprises the following steps: on the basis of gradient data obtained after Sobel operator filtering, determining an optimal edge segmentation threshold according to the length of an edge connected domain, and obtaining a gradient direction and a coordinate of an edge contour point as matching information; according to the contour point matching degree, searching defect points in a mode of combining coarse screening and fine screening, and then marking a character defect area in a mode of searching a connected domain based on defect point filling. In order to enable the matching algorithm to meet the real-time requirement, a self-adaptive image pyramid algorithm and a search strategy of ending in advance through threshold judgment are adopted. Similarity measurement adopted by the method is not influenced by shielding, chaos, linear and nonlinear illumination changes and the like, so that the character printing defects can still be quickly and accurately positioned in an illumination change environment.

Description

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AI Technical Summary

Benefits of technology

A technology called RandomAngle Image Pyramid (RIP) has been developed for identifying characters with imperfections such as cracks or holes that may affect their appearance quality during manufacturing processes. By comparing these edges from different images taken at various angles, this system helps identify any flaws before they become apparent after production.

Problems solved by technology

Technics: Current techniques such as manually inspecting printouts involve subjective judgment that may result in incorrect results due to variations between operators who perform these tasks differently from each other. Additionally, there exist various technical problem addressed through this technique, including slow response times caused by visual observation, difficulty with achieving accurate detection over different light conditions, and vulnerability towards rotational movement when analyzing images obtained under varying levels of brightness.

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