Computer Vision, Graphics, and Image Processing
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
The Science of Fractal Images
The algebraic basis of mathematical morphology. I. dilations and erosions
Computer Vision, Graphics, and Image Processing
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Fast principal component analysis using fixed-point algorithm
Pattern Recognition Letters
Computer Vision and Image Understanding
A comparative study on multivariate mathematical morphology
Pattern Recognition
α-Trimmed lexicographical extrema for pseudo-morphological image analysis
Journal of Visual Communication and Image Representation
Multiple Resolution Texture Analysis and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring the Fractal Dimension of Signals: Morphological Covers and Iterative Optimization
IEEE Transactions on Signal Processing
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological operators on the unit circle
IEEE Transactions on Image Processing
Fractal Dimension of Color Fractal Images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fundamenta Morphologicae Mathematicae
Fundamenta Informaticae
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Mathematical morphology offers popular image processing tools, successfully used for binary and grayscale images. Recently, its extension to color images has become of interest and several approaches were proposed. Due to various issues arising from the vectorial nature of the data, none of them imposed as a generally valid solution. We propose a probabilistic pseudo-morphological approach, by estimating two pseudo-extrema based on Chebyshev inequality. The framework embeds a parameter which allows controlling the linear versus non-linear behavior of the probabilistic pseudo-morphological operators. We compare our approach for grayscale images with the classical morphology and we emphasize the impact of this parameter on the results. Then, we extend the approach to color images, using principal component analysis. As validation criteria, we use the estimation of the color fractal dimension, color textured image segmentation and color texture classification. Furthermore, we compare our proposed method against two widely used approaches, one morphological and one pseudo-morphological.