Real-time image segmentation for anomalies detection using SVM approximation

In this paper, we propose a method of implementation improvement of the decision rule of the support vector machine, applied to real-time image segmentation. We present very high speed decisions (approximately 10 ns per pixel) which can be useful for detection of anomalies on manufactured parts. We propose an original combination of classifiers allowing fast and robust classification applied to image segmentation. The SVM is used during a first step, pre-processing the training set and thus rejecting any ambiguities. The hyperrectangles-based learning algorithm is applied using the SVM classified training set. We show that the hyperrectangle method imitates the SVM method in terms of performances, for a lower cost of implementation using reconfigurable computing. We review the principles of the two classifiers: the Hyperrectangles-based method and the SVM and we present our combination method applied on image segmentation of an industrial part.