TY - JOUR
T1 - A review of statistical approaches to level set segmentation
T2 - Integrating color, texture, motion and shape
AU - Cremers, Daniel
AU - Rousson, Mikael
AU - Deriche, Rachid
N1 - Funding Information:
We thank Christoph Schnörr for helpful comments on the manuscript. We also thank Zhizhou Wang for fruitful discussions, and Thomas Brox, Rodrigo de Luís García, Christophe Lenglet, Gianfranco Doretto, Paolo Favaro, Stefano Soatto and Anthony Yezzi for providing image examples of their methods. This work was partly supported by the German Research Foundation (DFG), grant #CR-250/1-1.
PY - 2007/3
Y1 - 2007/3
N2 - Since their introduction as a means of front propagation and their first application to edge-based 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 region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based 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 edge-based schemes such as the classical Snakes, region-based 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 well-suited 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.
AB - Since their introduction as a means of front propagation and their first application to edge-based 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 region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based 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 edge-based schemes such as the classical Snakes, region-based 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 well-suited 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.
KW - Bayesian inference
KW - Color
KW - Image segmentation
KW - Level set methods
KW - Motion
KW - Texture
UR - http://www.scopus.com/inward/record.url?scp=33846221503&partnerID=8YFLogxK
U2 - 10.1007/s11263-006-8711-1
DO - 10.1007/s11263-006-8711-1
M3 - Article
AN - SCOPUS:33846221503
SN - 0920-5691
VL - 72
SP - 195
EP - 215
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 2
ER -