Authors:
Mohamed Amine Mezghich
;
Ines Sakly
;
Slim Mhiri
and
Faouzi Ghorbel
Affiliation:
University of Manouba, Tunisia
Keyword(s):
Active Contours, Prior Knowledge, Shape Descriptors, Linear Discriminant Analysis, Estimation-Maximization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Features Extraction
;
Geometry and Modeling
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image-Based Modeling
;
Medical Image Applications
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Shape Representation and Matching
;
Software Engineering
;
Video Stabilization
;
Video Surveillance and Event Detection
Abstract:
We propose in this paper a new method of active contours with statistical shape prior. The presented approach
is able to manage situations where the prior knowledge on shape is unknown in advance and we have to
construct it from the available training data. Given a set of several shape clusters, we use a set of complete,
stable and invariants shape descriptors to represent shape. A Linear Discriminant Analysis (LDA), based on
Patrick-Fischer criterion, is then applied to form a distinct clusters in a low dimensional feature subspace. Feature
distribution is estimated using an Estimation-Maximization (EM) algorithm. Having a currently detected
front, a Bayesian classifier is used to assign it to the most probable shape cluster. Prior knowledge is then constructed
based on it’s statistical properties. The shape prior is then incorporated into a level set based active
contours to have satisfactory segmentation results in presence of partial occlusion, low contrast and noise.