Accession Number:

ADA463105

Title:

Shape-Based Approach to Robust Image Segmentation Using Kernel PCA

Descriptive Note:

Conference paper

Corporate Author:

GEORGIA INST OF TECH ATLANTA SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING

Report Date:

2006-01-01

Pagination or Media Count:

9.0

Abstract:

Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work of Leventon et al., we revisit the use of principal component analysis PCA to introduce prior knowledge about shapes in a more robust manner. To this end, we utilize Kernel PCA and show that this method of learning shapes outperforms linear PCA, by allowing only shapes that are close enough to the training data. In the proposed segmentation algorithm, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, clutter, partial occlusions, or smearing.

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE