Open AccessA Study on Region-Based Image Segmentation Techniques
Author(s) -
D. N. Sonar,
J. R. Kawale
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-2676
Subject(s) - mean shift , image segmentation , watershed , segmentation , scale space segmentation , region growing , artificial intelligence , cluster analysis , segmentation based object categorization , pattern recognition (psychology) , image (mathematics) , computer science , computer vision , mathematical morphology , minimum spanning tree based segmentation , mathematics , image processing
This paper addresses the comparison of two popular region-based image segmentation techniques namely the Watershed method and the Mean-shift method. The watershed method is a mathematical morphology based image segmentation approach while 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. Here the efficiency of the both segmentation techniques is presented