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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Automatic bootstrapping and tracking of object contours.

John Chiverton¹, Xianghua Xie, Majid Mirmehdi

¹School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand. jpchiverton@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 13, 2011

Summary

This summary is machine-generated.

A novel framework enhances automatic object tracking and segmentation using motion-based bootstrapping and a shape-based active contour with finite shape memory. This approach improves initialization and tracking performance, outperforming fixed-shape methods.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Object tracking and segmentation are crucial in image analysis.
Fully automatic methods require robust initialization and adaptive shape representation.
Existing methods may struggle with initialization or require extensive shape memory.

Purpose of the Study:

To introduce a fully automatic object tracking and segmentation framework.
To develop a finite shape memory mechanism for active contours.
To propose novel methods for automatic shape information extraction.

Main Methods:

A motion-based bootstrapping algorithm integrated with a shape-based active contour.
A finite shape memory that is continuously updated and forgets irrelevant information.
Two new techniques for automatic shape information extraction from image data.

Main Results:

The bootstrapping stage provides essential motion and shape information for active contour initialization.
The finite shape memory demonstrates convergence properties.
The proposed framework achieves comparable or superior performance to trackers with unlimited or fixed shape memory.

Conclusions:

The bootstrapped finite-shape-memory framework enables fully automatic and robust object tracking and segmentation.
The adaptive shape memory is efficient and effective, outperforming traditional methods.
The proposed approach offers a significant advancement in automated image analysis.