Authors:
Renato Dedić
1
and
Madjid Allili
2
Affiliations:
1
Université de Sherbrooke, Canada
;
2
Bishop’s University, Canada
Keyword(s):
Topology Preserving GDM, Level Sets, Deformable Models, Texture, Edge Detection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
;
Surface Geometry and Shape
Abstract:
Geometric deformable models (GDM) using the level sets method provide a very efficient framework for image segmentation. However, the segmentation results provided by these models are dependent on the contour initialization. Moreover, sometimes it is necessary to prevent the contours from splitting and merging in order to preserve topology. In this work, we propose a new method that can detect the correct boundary information of segmented objects while preserving topology when needed. We adapt the stoping function g in a way that allows us to control the contours’ topology. By analyzing the region where the edges of the contours are close we decide if the contours should merge, split or remain the way they are. This new formulation maintains the advantages of standard (GDM). Moreover,the topology-preserving constraint is enforced efficiently therefore, the new algorithm is only slightly computationally slower over standard (GDM).