Image Segmentation And Edge Detection Based On Watershed Techniques

Abstract

K-means and watershed segmentation techniques are presented to perform image segmentation and edge detection tasks. We first used the K-means technique to obtain a primary segmented image. We then employed a watershed technique that works on that image; this process includes gradient of the segmented input image, divides the image into markers, completes the watershed line by using the markers, and stores the image in the format of region adjacency graph (RAG). The initial segmentation result was obtained by the watershed algorithm. We then used merging techniques based on mean gray values and two edge strengths (Ti, T2) to obtain edge maps. In this article we solved the problem of undesirable oversegmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps have no broken lines on the entire image, and the final edge detection result is one closed boundary per actual region in the image.