Image segmentation is an important and fundamental task in many digital image processing systems. Image segmentation by thresholding is the simplest technique and involves the basic assumption that objects and background in the digital image have distinct gray-level distributions. In this paper, we present a general technique for thresholding of digital images based on Renyi's entropy. Our method includes two of the previously proposed well known global thresholding methods. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.
Renyi entropy
Shannon entropy Image thresholding Maximum entropy sum method Entropic correlation method 547-561. University of California Press, Berkeley (1961). About the Author--PRASANNA SAHOO received his Ph.D. degree in Applied Mathematics from the University of Waterloo, Canada in 1986. He joined the staff of the University of Louisville in 1988, where he is an Associate Professor of Mathematics. He has authored more than 60 archival journal papers in areas such as functional equations, geometry, cryptography, mathematical economics and digital image processing. About the Author--CARRYE WILKINS is a mathematics instructor at the University of Louisville, where she received her M.A. degree in Mathematics in 1994. Her research interest is in computer vision and its applications. About the Author--JERRY YEAGER received his M.A. degree in Mathematics in 1991. He is currently a doctoral candidate in Urban and Public Affairs at the University of Louisville.