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Updated: Jul 18, 2026
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
A luminance- and contrast-invariant edge-similarity measure
Saravana Kumar1, Sim Heng Ong, Surendra Ranganath
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
Summary
A new edge-detection similarity measure is robust to lighting and contrast changes. It improves noise performance and edge localization using specialized filters and regularization.
Area of Science:
- Image processing
- Computer vision
- Signal processing
Background:
- Edge detection is crucial for image analysis.
- Classical methods struggle with variations in luminance and contrast.
- Improved noise performance and edge localization are needed.
Purpose of the Study:
- Introduce a novel similarity measure for robust edge detection.
- Enhance performance under varying luminance and contrast conditions.
- Improve noise performance and edge localization.
Main Methods:
- Developed a novel similarity measure incorporating a regularization term.
- Employed directional Finite Impulse Response (FIR) edge filters.
- Utilized hyperbolic tangent profiles for filter design.
Main Results:
- The proposed measure demonstrates robustness to luminance and contrast variations.
- Achieved superior noise performance compared to classical edge-detection methods.
- Showcased enhanced edge localization accuracy.
Conclusions:
- The novel similarity measure offers significant advantages for edge detection.
- The method is effective in challenging imaging conditions.
- Represents a notable advancement in image analysis techniques.
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