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Updated: May 29, 2026

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

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Published on: August 30, 2013

Scale Invariant cosegmentation for image groups.

Lopamudra Mukherjee¹, Vikas Singh, Jiming Peng

¹Math. & Computer Science, Univ. of Wisconsin-Whitewater.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

|September 17, 2011

Summary

This summary is machine-generated.

This study introduces an efficient algorithm for multi-image cosegmentation, accurately identifying common foregrounds across many images. The method ensures scale invariance and near-linear runtime, overcoming previous computational challenges in computer vision.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Cosegmentation aims to segment common foregrounds in multiple images.
Existing methods struggle with scale invariance and computational complexity for large image sets.
Optimal solutions for even two images are often computationally prohibitive.

Purpose of the Study:

To generalize cosegmentation to a large number of images.
To achieve scale invariance for foregrounds of varying sizes.
To develop an algorithm with near-linear runtime complexity concerning the number of images.

Main Methods:

A novel, easy-to-implement algorithm is presented.
The approach addresses challenges in appearance modeling and problem size.
Focuses on concurrent segmentation of common foreground regions.

Main Results:

The algorithm performs well across multiple images.
Achieves scale invariance for foreground detection.
Demonstrates low computational requirements and near-linear scalability.

Conclusions:

The proposed algorithm effectively solves the multi-image cosegmentation problem.
It meets the requirements of scale invariance and computational efficiency.
Offers a viable solution for large-scale image analysis tasks.