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Measures of angularity in digital images
1Department of Psychology, Brandon University, 270 - 18th St, Brandon, MB, R7A 6A9, Canada. watiern@brandonu.ca.
Behavior Research Methods
|April 30, 2024
Summary
This study introduces four digital image processing techniques to objectively differentiate between angular and curvilinear shapes. The developed metrics effectively quantify image angularity, aiding research into curvature
Area of Science:
- Computer Vision
- Image Analysis
- Perceptual Psychology
Background:
- Growing interest in the affective and aesthetic qualities of curvature.
- Need for objective methods to analyze shape attributes in visual stimuli.
Purpose of the Study:
- To describe four digital image processing techniques for discriminating angular and curvilinear stimuli.
- To evaluate the efficacy of five derived metrics in quantifying image angularity.
Main Methods:
- Development of four digital image processing techniques implemented in MATLAB.
- Analysis of simple polygons, artistic drawings, real-world objects, typefaces, and abstract patterns.
- Application of logistic regression models to assess metric importance.
Main Results:
- Five metrics were derived from the four techniques to quantify image angularity.
- All metrics, except one, successfully distinguished between angular and curvilinear items above chance levels.
- Metric performance varied, influenced by image characteristics.
Conclusions:
- The developed digital image processing techniques and metrics offer objective tools for shape analysis.
- The study highlights the varying discriminative power of different metrics based on image content.
- Provides practical recommendations for utilizing these metrics in research on curvature perception.
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