Journal ArticleDOI
A combined Markov random field and wave-packet transform-based approach for image segmentation
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TLDR
A novel segmentation algorithm is compared with nonmultiresolution Markov random field-based image segmentation algorithms in the context of synthetic image example problems, and found to be both significantly more efficient and effective.Abstract:
The author formulates a novel segmentation algorithm which combines the use of Markov random field models for image-modeling with the use of the discrete wavepacket transform for image analysis. Image segmentations are derived and refined at a sequence of resolution levels, using as data selected wave-packet transform images or "channels". The segmentation algorithm is compared with nonmultiresolution Markov random field-based image segmentation algorithms in the context of synthetic image example problems, and found to be both significantly more efficient and effective. >read more
Citations
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Texture Analysis Methods - A Review
TL;DR: Methods for digital-image texture analysis are reviewed based on available literature and research work either carried out or supervised by the authors.
Journal ArticleDOI
Wavelet-based image denoising using a Markov random field a priori model
M. Malfait,Dirk Roose +1 more
TL;DR: A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods.
Journal ArticleDOI
K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation
TL;DR: The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions.
Proceedings ArticleDOI
Fast color/texture segmentation for outdoor robots
TL;DR: A compact color and texture descriptor has been developed and used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images and results of applying this descriptor for segmenting a synthetic image are presented.
Journal ArticleDOI
Empirical Bayes approach to improve wavelet thresholding for image noise reduction
Maarten Jansen,Adhemar Bultheel +1 more
TL;DR: In this article, a geometrical prior model for configurations of important wavelet coefficients is introduced and combined with local characterization of a classical threshold procedure into a Bayesian framework, where the local characterization is incorporated into the conditional model, whereas the prior model describes only configurations.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI
On the statistical analysis of dirty pictures
TL;DR: In this paper, the authors proposed an iterative method for scene reconstruction based on a non-degenerate Markov Random Field (MRF) model, where the local characteristics of the original scene can be represented by a nondegenerate MRF and the reconstruction can be estimated according to standard criteria.
Book
An introduction to wavelets
TL;DR: An Overview: From Fourier Analysis to Wavelet Analysis, Multiresolution Analysis, Splines, and Wavelets.
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