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Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Published on: February 25, 2021

Skeletal shape abstraction from examples.

M Fatih Demirci¹, Ali Shokoufandeh, Sven J Dickinson

¹Department of Computer Engineering, TOBB University of Economics and Technology, Sogutozu Cad. No.: 43, Ankara 06560, Turkey. mfdemirci@etu.edu.tr

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 21, 2009

Summary

This summary is machine-generated.

This study introduces a novel method for learning shape prototypes from 2D object exemplars. It overcomes limitations of one-to-one feature matching by using many-to-many correspondences for robust shape abstraction.

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Area of Science:

Computer Vision
Machine Learning
Computational Geometry

Background:

Object categorization research faces challenges in learning class prototypes from exemplars.
Existing methods often assume one-to-one feature correspondence, limiting true shape abstraction.

Purpose of the Study:

To develop a new technique for learning abstract shape prototypes from 2D shapes.
To handle many-to-many correspondence among local features in shape exemplars.

Main Methods:

Representing 2D shapes using medial axis graphs, where nodes are 'parts' and edges connect adjacent parts.
Establishing many-to-many node correspondence between medial axis graphs to identify articulating parts.
Recovering an abstracted medial axis graph with positional and radial attributes.

Main Results:

The proposed method successfully learns abstracted shape prototypes from sets of 2D exemplars.
Evaluation in a recognition task demonstrates the effectiveness of the abstracted prototypes.

Conclusions:

The new technique enables learning shape abstractions by addressing many-to-many feature correspondences.
This approach advances object categorization by enabling more robust shape prototype learning.