A hardware architecture for fast video object recognition using SVM and Zernike Moments

An architecture for fast video object recognition is proposed. This architecture is based on an approximation of feature-extraction function: Zernike moments and an approximation of a classification framework: Support Vector Machines (SVM). We review the principles of the moment-based method and the principles of the approximation method: dithering. We evaluate the performances of two moment-based methods: Hu invariants and Zernike moments. We evaluate the implementation cost of the best method. We review the principles of classification method and present the combination algorithm which consists in rejecting ambiguities in the learning set using SVM decision, before using the learning step of the hyperrectangles-based method. We present result obtained on a standard database: COIL-100. The results are evaluated regarding hardware cost as well as classification performances.