MOVING TOWARD REGION-BASED IMAGE SEGMENTATION TECHNIQUES: A STUDY
S. V. Kasmir Raja, Abdelkrim Khadir, S. S. Riaz Ahamed · 2009
Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application; the evaluation results may not be extensible to other applications. In this paper, we compare the performances of the two popular region-based image segmentation methods namely the Watershed method and the Mean-shift method. The watershed method, also called the watershed transform, is an image segmentation approach based on mathematical morphology. Mean-shift method is a data-clustering method that searches for the local maximal density points and then groups all the data to the clusters defined by these maximal density points. Keywords: Watershed Method (WS), Mean-Shift Method (MS) 1.