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Shape-Based Approach to Robust Image Segmentation using Kernel PCA.

Samuel Dambreville¹, Yogesh Rathi, Allen Tannenbaum

¹Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, USA 30332.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

|May 21, 2013

Summary

This summary is machine-generated.

This study introduces a novel shape-driven segmentation method using Kernel Principal Component Analysis (KPCA) for robust object separation. The technique effectively combines image data with learned shape priors, outperforming traditional methods.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Image segmentation is crucial for separating objects from backgrounds.
Traditional methods often struggle with noise, clutter, and occlusions.
Incorporating prior shape knowledge can improve segmentation accuracy and robustness.

Purpose of the Study:

To develop a novel segmentation method integrating image information with prior shape knowledge.
To enhance robustness against noise, clutter, and partial occlusions.
To outperform linear Principal Component Analysis (PCA) in learning shape priors.

Main Methods:

Utilized a level-set framework for segmentation.
Employed Kernel Principal Component Analysis (KPCA) for robust shape prior learning.
Encoded shape knowledge and image information into energy functionals.

Main Results:

Kernel PCA demonstrated superior performance over linear PCA in learning shape priors.
The proposed method achieved promising segmentation results.
The technique successfully handled noise, clutter, partial occlusions, and smearing.

Conclusions:

The novel shape-driven segmentation method offers improved robustness and accuracy.
Kernel PCA provides a more effective way to incorporate prior shape knowledge.
This approach facilitates simultaneous encoding of multiple shape types for segmentation.