Image recognition method and system based on edge geometric features

Abstract

The invention discloses an image recognition method based on edge geometric features. The image recognition method based on the edge geometric features comprises the steps that filtering processing is carried out on an original image of a workpiece; binarization processing is carried out on the image after the filter processing; the image after the binarization processing is searched to obtain a contour sequence set; a qualified workpiece contour is screened out of the contour sequence set; a minimum enclosing rectangle of the workpiece contour is determined, and the central point of the workpiece contour is determined; four subsequences of the workpiece contour are intercepted in the directions of the four edges of the minimum enclosing rectangle; energy values of all the four subsequences are calculated by using edge geometric feature operators; the subsequence with the maximum energy value in the four subsequences is determined, and direction angles of the workpiece are determined. The invention further provides a corresponding image recognition system. According to the image recognition method and system based on the edge geometric features, a template database of workpieces does not need to be established, storage consumption is lowered, calculation time is reduced, the requirement for high speed real-time performance can be met, and meanwhile the image recognition method and system have high fast recognition capability.

Description

technical field [0001] The invention belongs to the technical field of machine vision image processing, and more specifically relates to an image recognition method and system based on edge geometric features. Background technique [0002] Machine vision is one of the important research fields of computer science. It develops very rapidly by combining optical, mechanical and electrical comprehensive application of detection and recognition technology. The main research areas include image feature detection, contour representation, feature-based segmentation, distance image analysis, shape model and representation, stereo vision, motion analysis, color vision, active vision, self-calibration system, object detection, 2D and 3D object recognition and positioning etc. Its application range is also expanding day by day, involving many fields such as robotics, industrial inspection, object recognition, medical image analysis, military navigation and traffic management. [0003]...

AI Technical Summary

Problems solved by technology

However, for the application of automatic assembly in industrial production, it is difficult to adapt to the harsh on-site working environment and the requirements of processing technology, and the practical ability is limited.

For other improved practical algorithms, it is necessary to first analyze the target to be detected and establish a template database. In the detection, the optimal matching result must be found through traversal template matching, and the space complexity and time complexity will increase.

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