A Topology fixated Shape Gradient Framework for Non Simple Boundary Extraction for CIE Lab color images with Repulsive Energy
For researchers in image segmentation, this work offers a topology-controlled approach to segment complex images with multiple boundaries, though it is incremental as it modifies existing shape gradient methods.
The paper proposes a hybrid image segmentation method using a modified Mumford-Shah shape functional with a repulsive function to handle disjoint regions and multiple boundaries, achieving effective segmentation on gray and color images including nested structures and astronomical objects.
A levelset free but a hybrid image segmentation approach based on a modified version of the piece wise constant shape gradient of an Mumford Shah shape functional and a repulsive function is considered. The segmentation is performed a non-local shape based through an evolution of discrete curves driven by a non local shape based energy to segment images containing disjoint regions and multiple boundaries. This formulation has a novel additional component as a multivariable function dependent on a few sampled points of the curves that handles the occurrence of self intersection during boundary curves evolution. The method is applied to a few gray scale and color images, including images with nested structures and astronomical objects. The results indicate effective segmentation in complex scenarios with absolute control on the topology of the segments and self-intersections of the boundaries