Spatial Size Distributions: Applications to Shape and Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
Texture segmentation using wavelet transform
Pattern Recognition Letters
Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale morphological modeling of a class of structural texture
Machine Graphics & Vision International Journal
Texture image segmentation using combined features from spatial and spectral distribution
Pattern Recognition Letters
Texture classification using ridgelet transform
Pattern Recognition Letters
Texture classification using ridgelet transform
Pattern Recognition Letters
Viewpoint Invariant Texture Description Using Fractal Analysis
International Journal of Computer Vision
Plant Leaf Identification Using Multi-scale Fractal Dimension
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Rotation- and scale-invariant texture classification using log-polar and ridgelet transforms
Machine Graphics & Vision International Journal
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
Multifractal signature estimation for textured image segmentation
Pattern Recognition Letters
Fractal capacity dimension of three-dimensional histogram from color images
Multidimensional Systems and Signal Processing
Wavelet leader multifractal analysis for texture classification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A new recognition method for natural images
WSEAS Transactions on Computers
Plant leaf identification using color and multi-scale fractal dimension
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Computer Methods and Programs in Biomedicine
Texture features and segmentation based on multifractal approach
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Color texture analysis based on fractal descriptors
Pattern Recognition
Rotation-Invariant texture classification using steerable gabor filter bank
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Ambient Intelligence for Scientific Discovery
A simplified gravitational model to analyze texture roughness
Pattern Recognition
Ellipse Invariant Algorithm for Texture Classification
Fundamenta Informaticae
Texture recognition for frog identification
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Texture analysis and classification using shortest paths in graphs
Pattern Recognition Letters
A distinct and compact texture descriptor
Image and Vision Computing
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The Hurst parameter for two-dimensional (2-D) fractional Brownian motion (fBm) provides a single number that completely characterizes isotropic textured surfaces whose roughness is scale-invariant. Extended self-similar (ESS) processes were previously introduced in order to provide a generalization of fBm. These new processes are described by a number of multiscale Hurst parameters. In contrast to the single Hurst parameter, the extended parameters are able to characterize a greater variety of natural textures where the roughness of these textures is not necessarily scale-invariant. In this work, we evaluate the effectiveness of multiscale Hurst parameters as features for texture classification and segmentation. For texture classification, the performance of the generalized Hurst features is compared to traditional Hurst and Gabor features. Our experiments show that classification accuracy for the generalized Hurst and Gabor features are comparable even though the generalized Hurst features lower the dimensionality by a factor of five. Next, the segmentation accuracy using generalized and standard Hurst features is evaluated on images of texture mosaics. For these experiments, the performance is evaluated with and without supplemental contrast and average grayscale features. Finally, we investigate the effectiveness of the Hurst features to segment real synthetic aperture radar (SAR) imagery