IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
Direct methods for sparse matrices
Direct methods for sparse matrices
Mode boundary detection by relaxation for cluster analysis
Pattern Recognition
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Cluster Analysis by Binary Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A Fast Algorithm for Nonparametric Probability Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust automatic clustering scheme for image segmentation using wavelets
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
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Considering the analogy between image segmentation and cluster analysis, the aim of this paper is to adapt statistical texture measures to describe the spatial distribution of multidimensional observations. The main idea is to consider the cluster cores as domains characterized by their specific textures in the data space. The distribution of the data points is first described as a multidimensional histogram defined on a multidimensional regular array of sampling points. In order to evaluate locally a multidimensional texture, a co-occurrence matrix is introduced, which characterizes the local distribution of the data points in the multidimensional data space. Several local texture features can be computed from this co-occurrence matrix, which accumulates spatial and statistical information on the data distribution in the neighborhoods of the sampling points. Texture features are selected according to their ability to discriminate different distributions of data points. The sampling points where the local underlying texture is evaluated are categorized into different texture classes. The points assigned to these classes tend to form connected components in the data space, which are considered as the cores of the clusters.