Seminars/Colloquia/Invited Talks

Seminars

Chiu-Yen Kao

Minimization of Region-Scalable Fitting Energy for Image Segmentation

PLACE:	Clark 314
EVENT:	CIS Seminar Series
DATE:	September 25, 2007
TIME:	1:00 - 2:00 PM

Abstract

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. Popular region-based models, such as piecewise constant models, fail to segment such images without taking local intensity information into account. In this talk, we introduce a new region-based active contour model that draws upon intensity information in local regions at a controllable scale. A region-scalable fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is incorporated into a variational level set formulation with a level set regularization term. In the resulting curve evolution, intensity information in local regions at a certain scale can be extracted from a given image to compute the two fitting functions, which guides the motion of the contour toward the object boundaries. As a result, the proposed model can be used to segment images with intensity inhomogeneity. Regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and thus avoid expensive reinitialization procedures. Comparisons with the well-known piecewise smooth model show the advantages of our method in terms of computational efficiency and accuracy.

Brief Biography:

Chiu-Yen Kao is currently an assistant professor at The Ohio State University and a long term visitor at Mathematical Biosciences Institute. She received her Ph.D. in Mathematics from the University of California, Los Angeles in 2004. Her research expertise lies in level set methods, Hamilton-Jacobi equations, computational anatomy, and inverse problems.