Title :
Level Set Based Segmentation Using Local Feature Distribution
Author_Institution :
Dept. of Comput. Sci., Swansea Univ., Swansea, UK
Abstract :
We propose a level set based framework to segment textured images. The snake deforms in the image domain in searching for object boundaries by minimizing an energy functional, which is defined based on dynamically selected local distribution of orientation invariant features. We also explore the user initialization to simplify the segmentation and improve accuracy. Experimental results on both synthetic and real data show significant improvements compared to direct modeling of filtering responses or piecewise constant modeling.
Keywords :
filtering theory; image segmentation; image texture; set theory; filtering responses; level set based segmentation; local feature distribution; orientation invariant features; piecewise constant modeling; Active contours; Histograms; Image segmentation; Level set; Minimization; Nonhomogeneous media; Pixel; Active Contour; Image Segmentation; Level Set; Texture Analysis;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.681