Title :
Region segmentation via deformable model-guided split and merge
Author :
Liu, Lifeng ; Sclaroff, Stan
Author_Institution :
Dept. of Comput. Sci., Boston Univ., MA, USA
Abstract :
An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group´s bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that model-guided split and merge yields a significant improvement in segmention over a method that uses merging alone
Keywords :
computational geometry; image segmentation; merging; a priori distribution; bounding contour; deformable model-guided split and merge; deformable shape-based image segmentation; global deformation parameters; image regions; local shape properties; minimum description length principle; region segmentation; shape template; Computer science; Content based retrieval; Deformable models; Humans; Image retrieval; Image segmentation; Indexing; Merging; Object detection; Shape;
Conference_Titel :
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1143-0
DOI :
10.1109/ICCV.2001.937504
