A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation
DOI10.1142/S0219691314500441zbMath1341.60026arXiv1401.2686OpenAlexW2114723491MaRDI QIDQ5248168
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
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)
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