Image target area matching method based on Fast ICP

Abstract

The invention discloses an image target region matching method based on Fast ICP (Inductively Coupled Plasma), which comprises the following steps of: step 1, denoising the backgrounds of a template image and an acquired image: binarizing the image through adaptive threshold binarization and connectivity judgment based on a connected domain; step 2, realizing contour extraction of the template image and the acquired image through ray traversal and calculation of a regional edge gray value, and completing the determination of regional contour point coordinates; and step 3, Fast ICP matching: applying a Fast ICP algorithm to the contour point sets extracted from the template image and the acquired image to realize optimal matching of the acquired image with the template image. According to the Faast ICP-based image target area matching method, the position deviation of an acquired image in the prior art is overcome, template matching of the acquired image is realized, and the Faast ICP matching method completes matching in a rapid and effective manner, so that the efficiency of the whole method is greatly improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image target area matching method based on Fast ICP. Background technique [0002] In order to better improve the matching degree of the target area in the two images, it is necessary to denoise the background of the image and accurately extract the outline of the target area in the image, and finally the image matching can be completed. In order to further expand the application range of machine vision in the processing control process, it is not only necessary to implement common recognition and matching technologies, but also to study how to quickly and accurately obtain the position information of a specific point on the workpiece to be processed for control execution The mechanism performs more complex operations on the workpiece, such as soldering of special-shaped tiny electronic components. Aiming at the problem of plane two-dimensional shape matching, domest...

AI Technical Summary

Problems solved by technology

The genetic algorithm-based contour fuzzy feature matching method selects the genetic algorithm with global optimization characteristics to optimize, which can better express the constraints in contour matching and effectively reduce the occurrence of false matching, but the contour feature vector used is also Based on all contour points, it also requires more calculation time, and when using the genetic algorithm for optimization, the coding is not easy, and it is prone to premature phenomena. It is necessary to adjust the selection and mutation strategies according to specific problems, so there will be certain errors.

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