A local information based variational model for selective image segmentation
Jianping Zhang, Ke Chen, Bo Yu, Derek Alan Gould · Inverse Problems and Imaging · 2014
Many effective models are available forsegmentation of an image to extract all homogenous objects within it.For applications where segmentation of a single object identifiable bygeometric constraints within an image is desired, much less work has been done for this purpose.This paper presents an improved selective segmentation model, without`balloon' force, combining geometrical constraints and local image intensityinformation around zero level set, aiming to overcomethe weakness of getting spurious solutions by Badshah and Chen's model [8].A key step in our new strategy is an adaptive local band selection algorithm. Numerical experiments show that the new model appears to be able to detect an object possessing highly complex and nonconvex features, and toproduce desirable results in terms of segmentation quality and robustness.