Wei Zhenzhong

The application of machine vision in inspecting position-control accuracy of motor control systems

In this paper, a new structured-light machine vision technique based on a radial basis function (RBF) neural network is proposed and an inspection system is established. General structured-light machine vision techniques are usually based on accurate mathematical models and have some unavoidable and inexpressible errors. The proposed new technique is based on the training and learning of high-accuracy samples and overcomes the disadvantages of the general technique and considerably improves the accuracy of machine vision inspection systems. An experiment applying this new technique to inspect the position-control accuracy of a step-motor controlled stage with one linear translation axis shows that the RBF artificial neural network (ANN) is quite suited to structured-light machine vision inspection systems and that structured-light machine vision inspection techniques are really a novel and effective means for the inspection of the position-control accuracy of motor control systems.

An Application of Ant Colony Algorithm for Edge Feature Extraction in City Aerial Image for Building Recognition

Over the years, automatic and semiautomatic extraction of building from aerial and spatial images has become an outstanding issue. This paper presents an application of ant colony algorithm (ACA) for edge extraction in city aerial image for building recognition. The overall idea of this work is clustering pixels by a character complex of gray value, gradient and neighborhood of each pixel. The results of ACA will be compared with the results of Canny operator which is considered as the best edge feature extracting operator. And the comparison indicated that ACA is efficient in edge feature extraction, especially for the aerial images.