International Journal of Computer Vision
, Volume 72, Issue 2, pp 195215
First online:
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape
 Daniel CremersAffiliated withDepartment of Computer Science, University of Bonn Email author
 , Mikael RoussonAffiliated withDepartment of Imaging and Visualization, Siemens Corporate Research
 , Rachid DericheAffiliated withINRIA
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Since their introduction as a means of front propagation and their first application to edgebased segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of regionbased level set segmentation methods and clarify how they can all be derived from a common statistical framework.
Regionbased segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edgebased schemes such as the classical Snakes, regionbased methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly wellsuited for local optimization methods such as the level set method.
We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.
Keywords
image segmentation level set methods Bayesian inference color texture motion Title
 A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape
 Journal

International Journal of Computer Vision
Volume 72, Issue 2 , pp 195215
 Cover Date
 200704
 DOI
 10.1007/s1126300687111
 Print ISSN
 09205691
 Online ISSN
 15731405
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 image segmentation
 level set methods
 Bayesian inference
 color
 texture
 motion
 Industry Sectors
 Authors

 Daniel Cremers ^{(1)}
 Mikael Rousson ^{(2)}
 Rachid Deriche ^{(3)}
 Author Affiliations

 1. Department of Computer Science, University of Bonn, Germany
 2. Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ, USA
 3. INRIA, SophiaAntipolis, France