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Related Experiment Video

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Shape retrieval based on dynamic programming.

E Milios¹, E G Petrakis

¹Department of Computer Science, York University, Toronto, Ont. M3J 1P3, Canada. eem@cs.yorku.ca

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

|February 8, 2008

Summary

We developed a novel dynamic programming algorithm for matching deformed shapes. This shape matching method improves content-based retrieval accuracy for complex datasets.

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Content-based shape retrieval is challenging due to shape deformation.
Existing methods like Fourier descriptors and moment-based approaches have limitations in handling complex deformations.

Purpose of the Study:

To introduce a new shape matching algorithm using dynamic programming.
To enhance the accuracy and robustness of content-based shape retrieval for deformed shapes.

Main Methods:

Developed a dynamic programming algorithm for shape matching.
The algorithm groups finer-scale segments for correspondence with coarser scales.
Evaluated on 2-D datasets of hand gestures and marine life shapes.

Main Results:

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Demonstrated superior performance compared to traditional methods like Fourier descriptors and moments.
Successfully retrieved shapes based on content from diverse datasets.
Algorithm effectively handles shape deformation by hierarchical segmentation.

Conclusions:

The proposed dynamic programming approach offers a significant improvement for deformed shape matching and retrieval.
This method provides a robust solution for content-based image retrieval tasks involving complex shapes.