EVALUATION OF SPEED AND ACCURACY FOR COMPARISON OF TEXTURE CLASSIFICATION IMPLEMENTATION ON EMBEDDED PLATFORM

  • Tou, Jing Yi (Computer Vision and Intelligent Systems (CVIS) Group Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR)) ;
  • Khoo, Kenny Kuan Yew (Computer Vision and Intelligent Systems (CVIS) Group Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR)) ;
  • Tay, Yong Haur (Computer Vision and Intelligent Systems (CVIS) Group Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR)) ;
  • Lau, Phooi Yee (Instituto de Telecomunicacoes)
  • Published : 2009.01.12

Abstract

Embedded systems are becoming more popular as many embedded platforms have become more affordable. It offers a compact solution for many different problems including computer vision applications. Texture classification can be used to solve various problems, and implementing it in embedded platforms will help in deploying these applications into the market. This paper proposes to deploy the texture classification algorithms onto the embedded computer vision (ECV) platform. Two algorithms are compared; grey level co-occurrence matrices (GLCM) and Gabor filters. Experimental results show that raw GLCM on MATLAB could achieves 50ms, being the fastest algorithm on the PC platform. Classification speed achieved on PC and ECV platform, in C, is 43ms and 3708ms respectively. Raw GLCM could achieve only 90.86% accuracy compared to the combination feature (GLCM and Gabor filters) at 91.06% accuracy. Overall, evaluating all results in terms of classification speed and accuracy, raw GLCM is more suitable to be implemented onto the ECV platform.