Authors:
Mohamed Amine Mezghich
;
Slim M’Hiri
and
Faouzi Ghorbel
Affiliation:
École Nationale des Sciences de l’Informatique and ENSI, Tunisia
Keyword(s):
Active Contours, Shape Prior, Phase Correlation, Rigid Transformations, Invariant Descriptors.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Registration
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
In this paper, we intend to propose a new method to incorporate geometric shape prior into an edge-based active contours for robust object detection in presence of partial occlusions, low contrast and noise. A shape registration method based on phase correlation of binary images, associated with level set functions of the active contour and a reference shape, is used to define prior knowledge making the model invariant with respect to Euclidean transformations. In case of several templates, a set of complete invariant shape descriptors is used to select the most suitable one according to the evolving contour. Experimental results show the ability of the proposed approach to constrain an evolving curve towards a target shapes that may be occluded and cluttered under rigid transformations.