Region Segmentation via Deformable Model-Guided Split and Merge
Date
2000-12-04
DOI
Authors
Liu, Lifeng
Sclaroff, Stan
Version
OA Version
Citation
Liu, Lifeng; Sclaroff, Stan. "Region Segmentation via Deformable Model-Guided Split and Merge", Technical Report BUCS-2000-024, Computer Science Department, Boston University, December 4, 2000. [Available from: http://hdl.handle.net/2144/1817]
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. The quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. 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 the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.