A Luminance- and Contrast-Invariant Edge-Similarity Measure

A novel similarity measure for edge-detection that is robust to varying luminance and contrast is presented. It incorporates a regularization term and employs directional FIR edge filters with hyperbolic tangent profiles to ensure improved noise performance and edge localization compared to classica...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2006-12, Vol.28 (12), p.2042-2048
Hauptverfasser:	Kumar, Saravana, Ong, Sim Heng, Ranganath, Surendra, Chew, Fook Tim
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Computer science control theory systems Detectors Edge detection Exact sciences and technology Filtering Finite impulse response filter Gabor filters Goniometers Image converters Image edge detection Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Intelligence Light Lighting Localization Luminance Noise Noise measurement Noise robustness Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry projection angles Regularization Reproducibility of Results Sensitivity and Specificity Similarity similarity measure Subtraction Technique Tangents
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Beschreibung
Zusammenfassung:	A novel similarity measure for edge-detection that is robust to varying luminance and contrast is presented. It incorporates a regularization term and employs directional FIR edge filters with hyperbolic tangent profiles to ensure improved noise performance and edge localization compared to classical methods
ISSN:	0162-8828 1939-3539 2160-9292
DOI:	10.1109/TPAMI.2006.236