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Updated: May 19, 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

Invariant object recognition based on extended fragments.

Evgeniy Bart¹, Jay Hegdé

¹Palo Alto Research Center, Intelligent Systems Laboratory Palo Alto, CA, USA.

Frontiers in Computational Neuroscience

|September 1, 2012

Summary

This summary is machine-generated.

Human visual recognition can use object fragments for invariant perception, learning to compensate for changing illumination. This finding supports computational models of fragment-based recognition strategies.

Keywords:

form vision illumination constancy informative fragments invariant recognition mutual information

Related Experiment Videos

Last Updated: May 19, 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:

Cognitive Science
Neuroscience
Computer Vision

Background:

Object recognition is challenged by varying viewpoints and illumination.
Human visual systems effectively compensate for these changes, but strategies remain unclear.
Computational studies suggest object fragments, not whole objects, can enable invariant recognition.

Purpose of the Study:

To investigate if human observers use object fragments for illumination-invariant recognition.
To determine if the human visual system can learn to compensate for appearance changes using local features.
To test the correlation between fragment information content and recognition performance across illuminations.

Main Methods:

Subjects trained to recognize novel 3-D "digital embryos" across different illuminations.
Recognition performance tested using object fragments after initial training.
Mutual information (MI) of fragments, considering illumination variations, calculated and correlated with performance.

Main Results:

Human observers achieved illumination invariance using object fragments.
Recognition performance strongly correlated with the mutual information (MI) of fragments under varying illumination.
This correlation was independent of task difficulty variations across fragments.

Conclusions:

Human visual recognition can utilize local features (fragments) for invariance, compensating for appearance changes.
The visual system can learn to adapt to illumination variations, not solely relying on pre-existing invariant features.
Findings align with computational models predicting fragment-based invariant object recognition.