Object Classfification in Computer Vision with Discriminant Analysis - Hamzahan, Amir (UI Scholars Hub, 2002)
A robotic sensor system is always supported by a computer system called ‘computer vision’. The important concept of computer vision is object classfifi cation. In this study two algorithms for object classifi cation in this system will be compared. Firstly, A simple method that do not need complex computation and that considered as an informal method is called binary tree decision structure. This method is based on modest caracteristic decriptors of an object such as vertical line, horizontal line or ellipse line. Unfortunately this method has weakness in recognize an image that contaminated by a noise. Secondly, a more formal method with high variability descriptors. In this contect a multivariate statistical approach named discriminant analysis is proposed as an alternative for object classifi cation. This method is operated by computation of a function called Fisher discriminant function that can be used for separating an object. From the data simulation and analysis for calssifi cation of two object i.e. screw and bolt and three objects i.e. alphabet T,O and S it can be shown that discriminant analysis approach can classify an object better than binary decision algorithm. The superority of discriminant method is especially seen when this method is applied for classifi cation of a noisy image of object.