Dimensional Complex Geometric Features Of Machine Vision Coordinated Measurements

As a new measurement with obvious features such as non-contact, high precision, fast measurement, automation, intelligence, abundant information amount, machine vision measurement (MVM) has been widely used in high precision measurement of industrial parts in recent years.To realize automatic measurement of two dimensional complex geometric characteristics on parts with high-precision, after studying on some key techniques such as three stepper motors linkage control based on single chip, image edge detection, reticle extraction of line scale, image matching, morphological image processing and multi-FOV image coordinate relationship of collaborative measurement based on machine vision, evaluation of uncertainty in measurement, a system of collaborative measurement based on machine vision is designed in this paper.(1) The first step of collaborative measurement method is collect large FOV image of measured objects to provide the preliminary location of these auxiliary features, secondly the small field of view image acquisition path is planned by the information of large field of view, and then extract parameters of small FOV images, final obtain measured information. So the system of collaborative measurement based on machine vision consist of the image collecting subsystem, the mechanical structure of subsystem, three stepper motors are controlled at the same time by AVR single chip ATmegal6(motion control subsystem)and image processing subsystem based on Matlab2012is designed.(2) Study on image processing of vision measurement. Firstly, some common binary image, edge detection, morphological image processing are studied, secondly, a new edge detection algorithm is proposed based on variance of adjacent pixels gray and method of morphological image processing, finally, the differences of detection results among proposed algorithm, Canny algorithm, Sobel algorithm, Robert algorithm and Prewitt algorithm are discussed, the result show that proposed algorithm has fewer false edges and no edge deletion.(3) Collaborative measurement of hole-series features. Firstly, the mapping relation between large FOV image coordinate and the object space points coordinate, between large FOV image coordinate and small FOV image coordinate, between small FOV image coordinate and small FOV image coordinate are discussed; secondly, applied the hole distance of large FOV and the image size of small FOV,pixel equivalent value, then the path of small FOV is planned, finally, used cross-correlation coefficient method and least square method to process sequence images of small FOV images to obtain dimension of hole distance.(4) Evaluation of uncertainty in measurement. First the evaluation method of uncertainty of measurement is studied; then analysis uncertainty components of measurement results of hole-series characteristic; finally explain the reliable degree of measurement results of hole-series feature.