Open AccessPerformance comparison and evaluation of vari-ous segmentation methods
Author(s) -
Vikashini Venkatesh,
P Praveen
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.9687
Subject(s) - artificial intelligence , image segmentation , computer science , segmentation , computer vision , pixel , image texture , scale space segmentation , pattern recognition (psychology) , image (mathematics) , region growing , range segmentation , image processing , segmentation based object categorization
Image segmentation is the most important method in the concept of image processing. It helps in analyzing the image accurately in many applications. It is generally used to assign or name, a label to individual pixels in an image, so that labels with similar name share common features. These related pixels result in same color, texture, or intensity. It also helps in identifying lines, curves and objects. These kinds of results help in different applications in the field of medical imaging, 3D constructions, etc. There are different kinds of segmentation methods already available for such applications. This paper briefs and compares three different types of segmentation methods like multithreshold method, watershed method and normalized cut method. It is compared based on computational time, complexity and number of clusters of the different methods used in the image.