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Related Experiment Video

Updated: Jul 9, 2026

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

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

Published on: August 30, 2013

One-dimensional logarithmic harmonic synthetic discriminant function filters for shift-, scale-, and

J Yao, G Lebreton

|December 18, 2007

Summary

This study presents a novel pattern recognition method combining harmonic expansion and synthetic discriminant functions. This approach achieves invariance to shifts, scale, and projection, significantly reducing training data needs.

Related Experiment Videos

Last Updated: Jul 9, 2026

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

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

Published on: August 30, 2013

Area of Science:

Pattern recognition
Image processing
Signal processing

Background:

Traditional pattern recognition methods often struggle with variations in scale, orientation, and projection.
Synthetic discriminant function (SDF) filters are effective for invariant pattern recognition but typically require extensive training data.

Purpose of the Study:

To develop a novel approach for shift-, scale-, and projection-invariant pattern recognition.
To reduce the number of training images required compared to classical SDF filters.

Main Methods:

The proposed method integrates harmonic expansion with synthetic discriminant function (SDF) approaches.
A specialized SDF filter utilizing equal-order one-dimensional logarithmic harmonic components is employed.

Main Results:

The combined approach ensures projection invariance in one direction due to the harmonic components.
This method significantly decreases the number of training images needed for effective pattern recognition.

Conclusions:

The novel approach offers a more efficient solution for invariant pattern recognition.
Combining harmonic expansion and SDF filters provides a powerful tool for handling image variations with reduced data requirements.