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Updated: Jun 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

Applying property testing to an image partitioning problem.

Igor Kleiner¹, Daniel Keren, Ilan Newman

¹Department of Computer Science, University of Haifa, Israel.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 25, 2010

Summary

This summary is machine-generated.

Property testing offers efficient image analysis by quickly rejecting inputs unlikely to match a template. This approach saves significant computational time in image detection tasks.

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Area of Science:

Computer Science
Image Processing
Algorithm Design

Background:

Property testing is a computational method for efficiently verifying properties of large datasets.
Traditional property testing assumes most inputs do not satisfy a specific condition, enabling rapid rejection.
Applying property testing to image detection can significantly reduce processing time for large image files.

Purpose of the Study:

To adapt the property testing paradigm for efficient image detection.
To develop a fast algorithm for determining if an image conforms to a given template.
To introduce a method that quickly rejects images significantly different from the target template.

Main Methods:

Analysis of image partitioning based on template matching.
Development of a constant-time "rejector" algorithm.
Testing a sub-image extracted from the input, independent of input size.

Main Results:

The proposed "rejector" algorithm efficiently identifies images "far" from the template.
The algorithm's runtime and memory usage are independent of the input image size.
High probability of correctly rejecting dissimilar images.

Conclusions:

Property testing is a viable and efficient approach for image detection tasks.
The developed constant-time rejector significantly speeds up the process of identifying template-matching images.
This method offers substantial computational savings for large-scale image analysis.