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Parametric Optimization Design Method for Friction Plates of Hydro-Viscous Clutches

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Optimum design of chamfer distance transforms.

M Akmal Butt¹, P Maragos

¹Sch. of Electr. and Comput. Eng., Georgia Inst. of Technol., Atlanta, GA 30332, USA. akmal@ee.gatech.edu

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

|February 16, 2008

Summary

Chamfer distance transforms approximate Euclidean distances efficiently for image analysis. New geometric methods optimize local distances, reducing computation without sacrificing accuracy.

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Area of Science:

Digital Image Processing
Computer Vision
Computational Geometry

Background:

Distance transforms are crucial for image analysis.
Chamfer distance transforms offer efficient approximations to Euclidean distance transforms.
Integer-valued distances from chamfer transforms are beneficial for digital image processing.

Purpose of the Study:

To develop a novel geometric approach for determining optimal local distances in chamfer distance transforms.
To enhance the visualization and extensibility of chamfer metric calculations.
To introduce a concept of critical local distances for computational efficiency.

Main Methods:

A new geometric approach was developed to find optimal local distances.
The method was designed for easy visualization and extension to larger neighborhoods.
The concept of critical local distances was introduced to reduce complexity.

Main Results:

The new geometric approach provides optimal local distances for chamfer distance transforms.
The method is easily visualizable and extensible to chamfer metrics with large neighborhoods.
Critical local distances were identified, reducing computational complexity without increasing approximation error.

Conclusions:

The developed geometric approach offers an effective way to compute optimal local distances for chamfer distance transforms.
The introduction of critical local distances streamlines computation while maintaining accuracy.
This work advances the efficiency and applicability of chamfer distance transforms in image analysis.