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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence.

Fei Wang¹, Baba C Vemuri, Anand Rangarajan

¹Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611 USA.

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

|September 28, 2010

Summary

This summary is machine-generated.

We introduce a robust algorithm for groupwise non-rigid registration of point sets using a novel CDF-JS divergence measure. This method is statistically robust, computationally efficient, and ideal for creating shape atlases and registering 3D data.

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Area of Science:

Computer Vision
Medical Image Analysis
Computational Geometry

Background:

Non-rigid registration aligns shapes represented by point sets.
Existing methods often require labeled data or have biases.
Groupwise registration offers a more robust approach but faces challenges in divergence quantification.

Purpose of the Study:

To develop a novel, bias-free algorithm for groupwise non-rigid registration of multiple unlabeled point sets.
To introduce a new divergence measure, the CDF-JS divergence, for quantifying differences between probability distributions derived from point sets.
To enable efficient and accurate registration for applications like atlas creation and 3D data alignment.

Main Methods:

Developed the Cumulative Distribution Function-Jensen-Shannon (CDF-JS) divergence, a robust measure for probability distributions based on CDFs.
Derived the analytic gradient of the CDF-JS divergence for numerical optimization.
Implemented a groupwise registration algorithm using an evolving pooled model, minimizing CDF-JS for alignment without a fixed reference.
Validated the algorithm on 2D and 3D real point set data.

Main Results:

The proposed CDF-JS divergence is statistically more robust and immune to noise compared to traditional Jensen-Shannon divergence.
The groupwise registration algorithm is computationally efficient and accurate.
Demonstrated successful application in creating atlases of various shapes and simultaneous registration of 3D range data without correspondence.
Experimental results on real 2D/3D point set data validate the algorithm's effectiveness.

Conclusions:

The novel CDF-JS divergence provides a robust and bias-free measure for groupwise registration.
The developed algorithm offers an efficient and accurate solution for non-rigid registration of unlabeled point sets.
This approach is highly valuable for applications requiring the creation of shape atlases and the alignment of complex 3D datasets.