Journal ArticleDOI
Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields
Xiangyu Yang,Jun Liu +1 more
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TLDR
An unsupervised texture segmentation method with the one-step mean shift algorithm and the boundary Markov random field and the multilevel logistic distribution for smoothing regions with its characteristic of region forming is presented.About:
This article is published in Pattern Recognition Letters.The article was published on 2001-08-01. It has received 21 citations till now. The article focuses on the topics: Markov random field & Image segmentation.read more
Citations
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Journal ArticleDOI
Mean shift-based clustering
Kuo-Lung Wu,Miin-Shen Yang +1 more
TL;DR: A mean shift-based clustering algorithm that can solve bandwidth selection problems from a different point of view, as well as those of computational complexity, cluster validity and improvements of mean shift in large continuous, discrete data sets is proposed.
Proceedings Article
False-Peaks-Avoiding Mean Shift Method for Unsupervised Peak-Valley Sliding Image Segmentation
Hanzi Wang,David Suter +1 more
TL;DR: This paper shows both empirically and analytically that when using sample data, the reconstructed PDF may have false peaks and how the occurrence of the false peaks is related to the bandwidth h of the kernel density estimator, using examples of gray-level image segmentation.
Journal ArticleDOI
Fast and active texture segmentation based on orientation and local variance
TL;DR: A separability measurement method is used for selecting four feature images with good separability in four orientations, and a variational framework incorporating these features in a level set based, unsupervised segmentation process is adopted to improve the computational speed.
Proceedings ArticleDOI
Color image segmentation with watershed on color histogram and Markov random fields
Shengyang Dai,Yu-Jin Zhang +1 more
TL;DR: Experiments with real color images show that the proposed two-step segmentation framework is efficient and helps solve the problem of improper color clustering caused by color clusters with irregular shapes.
Journal ArticleDOI
Oriented Triplet Markov Fields
Jean-Baptiste Courbot,Jean-Baptiste Courbot,Emmanuel Monfrini,Vincent Mazet,Christophe Collet +4 more
TL;DR: This paper tackles the problem of anisotropic image modeling by introducing an Oriented Triplet Markov Field model, able to explicitly deal with oriented structures, and compares the behavior of this model towards other Markovian modeling on images containing such oriented pattern.
References
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
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.
Journal ArticleDOI
Texture features for browsing and retrieval of image data
B.S. Manjunath,Wei-Ying Ma +1 more
TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
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