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Texture sparseness, but not local phase structure, impairs second-order segmentation.

Elizabeth Zavitz¹, Curtis L Baker

¹McGill Vision Research, Department of Ophthalmology, McGill University, Montreal, Quebec, Canada; Department of Physiology, Monash University, Clayton, Victoria, Australia.

Vision Research

|August 15, 2013

Summary

This summary is machine-generated.

Higher-order texture statistics, not just low-level Fourier energy, significantly impact texture boundary segmentation. Manipulating texture properties like sparseness and phase structure reveals key factors influencing how humans perceive texture boundaries.

Keywords:

Higher-order statistics Natural images Second order Segmentation Texture

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Area of Science:

Vision science
Computational neuroscience
Image processing

Background:

Texture boundary segmentation is traditionally linked to low-order texture statistics.
Previous work indicated higher-order texture structure also influences segmentation.
The specific role of higher-order statistics remained unclear.

Purpose of the Study:

To investigate the influence of specific higher-order texture statistics on segmentation of contrast and orientation boundaries.
To determine how texture properties like sparseness and phase structure affect boundary perception.
To test a computational model's ability to explain these effects.

Main Methods:

Utilized naturalistic synthetic textures with manipulated sparseness, global phase structure, and local phase alignments.
Measured segmentation thresholds using forced-choice judgments of boundary orientation.
Developed and tested a two-stage filter model with nonlinearities.

Main Results:

Global phase scrambling significantly reduced segmentation thresholds for both contrast and orientation boundaries.
Decreasing texture sparseness also lowered segmentation thresholds.
Removing local phase alignments had minimal impact on segmentation thresholds.

Conclusions:

Higher-order texture statistics, particularly global phase structure and sparseness, play a crucial role in texture boundary segmentation.
A two-stage filter model with specific nonlinearities can account for the observed effects.
The model successfully predicts segmentation performance for both synthetic and natural textures.