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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Fourier coding of image boundaries.

R Chellappa¹, R Bagdazian

¹Department of Electrical Engineering and the Image Processing Institute, University of Southern California, Los Angeles, CA 90089.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces a novel transform coding scheme for closed image boundaries. The method utilizes discrete Fourier transform and a Gaussian model for efficient boundary data representation and compression.

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Representing and compressing image boundaries is crucial for efficient image storage and transmission.
Existing methods may struggle with accurately encoding complex or closed boundary shapes.

Purpose of the Study:

To develop an efficient transform coding scheme specifically for closed image boundaries.
To leverage the discrete Fourier transform (DFT) for decorrelating boundary data.
To implement a compression strategy using estimated Fourier coefficient variances.

Main Methods:

Approximating closed boundaries using line segments and representing them by coordinates or radial vectors.
Applying the discrete Fourier transform (DFT) to decorrelate boundary data.
Fitting a Gaussian circular autoregressive model to estimate Fourier coefficient variances.
Utilizing the MAX quantizer for implementing the coding scheme.

Main Results:

The proposed scheme effectively decorrelates finite boundary data using DFT.
Estimates of Fourier coefficient variances are obtained via a Gaussian circular autoregressive model.
The transform coding scheme is successfully implemented using these variances and the MAX quantizer.

Conclusions:

The developed transform coding scheme offers an efficient method for compressing closed image boundaries.
The integration of DFT and Gaussian modeling provides a robust approach to boundary data representation.
The scheme demonstrates practical applicability in image compression scenarios.