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Related Experiment Video

Updated: May 29, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Pseudodistance measures for recognition of curved objects.

Y Sato¹, I Honda

¹MEMBER, IEEE, Department of Electronic Engineering, Faculty of Technology, Tokyo University of Agriculture and Technology, Koganei, Tokyo 184, Japan; Department of Electrical Engin.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This study introduces a new method for describing and identifying curved objects by analyzing their shape as a surface composed of horizontal sections. The technique decomposes complex shapes into five key features for pattern recognition.

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Area of Science:

Computer Vision
Geometric Modeling
Pattern Recognition

Background:

Describing and identifying complex curved objects remains a challenge in computer vision and pattern recognition.
Existing methods may struggle with the intricate details and variations found in curved object shapes.

Purpose of the Study:

To develop a novel method for the accurate description and identification of curved objects.
To represent object shape as a surface derived from horizontal section boundaries.
To decompose complex shapes into a set of fundamental geometric features.

Main Methods:

Representing object shape using a Fourier series expansion of a representative function.
Decomposing object features into elongatedness, horizontal strain, section shape, torsion, and displacement.
Defining object differences as pseudodistances between these decomposed features.
Theoretically analyzing properties of the pseudodistance family, including completeness.

Main Results:

The proposed method successfully describes and identifies curved objects based on their decomposed features.
Experimental validation using wooden block models and a doll demonstrated the method's effectiveness.
The theoretical analysis confirmed desirable properties of the defined pseudodistance metric.

Conclusions:

The Fourier-based decomposition into five features provides a robust way to describe curved object shapes.
The pseudodistance metric enables effective pattern recognition and identification of curved objects.
This method offers a promising approach for applications requiring precise shape analysis and object recognition.