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Coarse-to-fine segmentation and tracking using Sobolev active contours.

Ganesh Sundaramoorthi¹, Anthony Yezzi, Andrea Mennucci

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. ganeshs@ece.gatech.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 29, 2008

Summary

This summary is machine-generated.

Sobolev active contours offer improved global evolution and coarse-to-fine scale motion, making them ideal for tracking algorithms. This method enhances traditional active contour tracking by minimizing local minima attraction.

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Area of Science:

Computer Vision
Image Analysis
Computational Geometry

Background:

Traditional active contours struggle with local minima and exhibit less global evolution.
Minimizing curve energies traditionally involves defining perturbation costs and gradients.

Purpose of the Study:

Analyze Sobolev active contours using scale-space analysis.
Understand the multi-scale evolution behavior of Sobolev active contours.
Demonstrate the suitability of Sobolev active contours for tracking applications.

Main Methods:

Scale-space analysis applied to Sobolev active contours.
Mathematical formulation of Sobolev active contours based on a Riemannian metric.
Experimental comparison of Sobolev active contours with traditional methods in tracking tasks.

Main Results:

Sobolev active contours exhibit continuous coarse-to-fine scale motion.
These contours are less prone to getting trapped in local minima.
The Sobolev metric demonstrates superior performance in tracking algorithms compared to traditional metrics.

Conclusions:

The coarse-to-fine property justifies Sobolev active contours for applications prioritizing large-scale deformations.
Sobolev active contours are particularly well-suited for active contour-based tracking algorithms.
Replacing traditional metrics with the Sobolev metric significantly improves active contour tracking methods.