OBJECT RECOGNITION SYSTEM USING TEMPLATE MATCHING BASED ON SIGNATURE AND PRINCIPAL COMPONENT ANALYSIS

In this paper an object recognition system using template matching is implemented. Since objects are represented by either an external or internal descriptors, a combination of using signature, principal component analysis and color is used. The system efficacy is measured by applying it to recognize an image of a chessboard with a set of objects (pieces). The output of the system includes the pieces names, locations and color. The signature feature is used to distinguish the pieces types based on their external shape but when it falls short, the principal components analysis is used instead. The object color is also obtained. The matching between features is carried out based on Euclidean distance metric .The proposed system is implemented, trained, and tested using Matlab based on a set of collected samples representing chessboard images. The simulation results show the effectiveness of the proposed method in recognizing the pieces locations, types, and color.