A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation

DOI10.1142/S0219691314500441zbMath1341.60026arXiv1401.2686OpenAlexW2114723491MaRDI QIDQ5248168

Kathryn Heal, Jérôme Gilles

Publication date: 27 April 2015

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1401.2686

zbMATH Keywords

segmentationhistogramsscale-spacemeaningful modes

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Signal detection and filtering (aspects of stochastic processes) (60G35) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (4)

Empirical Voronoi wavelets ⋮ Continuous Empirical Wavelets Systems ⋮ Automated Identification of Epileptic Seizures from EEG Signals Using FBSE-EWT Method ⋮ An efficient segmentation method based on dynamic graph merging

Cites Work

• Unnamed Item
• Algorithm AS 136: A K-Means Clustering Algorithm
• Maximal meaningful events and applications to image analysis
• From Gestalt theory to image analysis. A probabilistic approach.
• Automatic color palette
• 2D Empirical Transforms. Wavelets, Ridgelets, and Curvelets Revisited
• EXTRACTION OF NOISE TOLERANT, GRAY-SCALE TRANSFORM AND ROTATION INVARIANT FEATURES FOR TEXTURE SEGMENTATION USING WAVELET FRAMES
• Empirical Wavelet Transform
• A NOVEL APPROACH COMBINING GABOR WAVELET AND MOMENTS FOR TEXTURE SEGMENTATION

This page was built for publication: A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation