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Updated: Apr 30, 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

A sequential framework for image change detection.

Andrew J Lingg, Edmund Zelnio, Fred Garber

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

|May 13, 2014

Summary

This summary is machine-generated.

This study introduces a sequential change detection framework using multiple images to enhance performance. The developed change statistic improves detection accuracy with new data, even with unknown probability distributions.

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Last Updated: Apr 30, 2026

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Published on: June 19, 2017

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

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Published on: June 16, 2014

Area of Science:

Remote Sensing
Image Analysis
Data Science

Background:

Change detection is crucial for monitoring environments using image data.
Existing methods may not optimally leverage sequential or multi-source imagery.
Predicting performance with limited data distributions remains a challenge.

Purpose of the Study:

To develop a sequential framework for improved change detection.
To introduce an adaptable change statistic for evolving datasets.
To predict and validate detection performance with varying data distributions.

Main Methods:

A sequential framework integrating multiple reference and mission images.
Development of an easily updatable change statistic.
Performance prediction based on known data distributions and simulation.
Validation using synthetic aperture radar (SAR) and visible light imagery.

Main Results:

The sequential framework enhances change detection performance.
The proposed change statistic is efficiently updated with new data.
Detection performance is predictable for known data distributions.
Performance improvements were demonstrated on both SAR and visible image datasets, even with unknown distributions.

Conclusions:

The sequential framework offers a robust approach to change detection.
The adaptable statistic facilitates performance gains with increased data.
The predictability of performance aids in system design and evaluation.
The method is effective across different imaging modalities and data characteristics.