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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Segmenting surface boundaries using luminance cues.

Christopher DiMattina¹, Curtis L Baker²

¹Computational Perception Laboratory & Department of Psychology, Florida Gulf Coast University, Whitaker Hall Room 215, 10501 FGCU Blvd S., Fort Myers, FL, 33965-6565, USA. cdimattina@fgcu.edu.

Scientific Reports

|May 13, 2021

Summary

This summary is machine-generated.

Human visual perception can segment luminance texture boundaries using a two-stage Filter-Rectify-Filter model. This model explains performance better than simpler models, especially when visual noise is present.

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Area of Science:

Visual perception
Computational neuroscience
Image processing

Background:

Luminance differences are key cues for scene segmentation.
Luminance texture boundaries, arising from varying light/dark element proportions, pose a challenge for simple visual models.

Purpose of the Study:

Investigate human performance in segmenting luminance texture boundaries.
Evaluate the efficacy of different computational models for this task.

Main Methods:

Human observers performed segmentation tasks with luminance texture boundaries.
Tested single-stage filtering models with and without contrast normalization.
Introduced and validated a two-stage Filter-Rectify-Filter model.

Main Results:

A single-stage filter model fails to explain observer performance.
Contrast normalization improves the single-stage model but is insufficient.
The two-stage Filter-Rectify-Filter model accurately predicts human segmentation performance.

Conclusions:

Human visual segmentation of luminance texture boundaries likely involves a two-stage filtering process.
This model accounts for performance even with interfering luminance step boundaries.
The findings have implications for understanding visual processing in natural scenes with complex lighting.