Machine vision image characteristic point detection and matching combination optimization method

Abstract

The invention discloses a machine vision image characteristic point detection and matching combination optimization method. The method mainly comprises the steps of firstly obtaining a template image and a search image, and splicing the template image and the search image into a workpiece image; secondly performing characteristic point detection on the workpiece image to obtain P characteristic points; thirdly performing characteristic point description on the P characteristic points separately, namely, selecting any characteristic point as a center for constructing a pixel block image, and performing Gaussian filtering on sampling points contained in the pixel block image separately to obtain a sampling point pair corresponding to the characteristic point so as to obtain sampling point pairs corresponding to the P characteristic points and sampling point pair distances corresponding to the P characteristic points; and fourthly obtaining corresponding aggregated model directions of long-distance sampling point pairs and corresponding binary descriptors of short-distance sampling point pairs, performing matching identification on the P characteristic points separately, calculating an affine transformation parameter between the template image and the search image, obtaining three-dimensional coordinates of a target workpiece in the search image, and performing accurate capture.

Description

technical field [0001] The invention belongs to the field of machine vision detection, in particular to an optimization method for detection and matching of machine vision image feature points. Background technique [0002] The application of machine vision technology to the production and assembly of industrial robots improves the perception and adaptability of industrial robots to complex environments, and also improves the flexibility and automation of production and manufacturing. The machine vision system transmits image processing information to industrial robots for control Then the controller drives the industrial manipulator to grab the target workpiece. In image processing, due to the influence of factors such as imaging distance, direction, and position, the image will be rotated, translated, and scaled. The feature of the image point can be compared with To avoid the above problems, the detection and matching of feature points is the key to image processing. Rapi...

AI Technical Summary

Problems solved by technology

[0005] The accelerated robust feature (SURF) feature point extraction algorithm can maintain scale invariance, and at the same time detect more feature points, and the speed is faster, but it is also relatively random when matching, and it is easy to generate more wrong matching point pairs. The binary robust scale-invariant feature point (BRISK) feature point extraction algorithm is relatively accurate, but its matching rate is low, that is, the time required for matching is long, and the matching feature point pairs are relatively small.

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