Proceedings ArticleDOI
Rapid Texture Identification
Kenneth I. Laws
- Vol. 0238, pp 376-381
TLDR
In this article, the texture energy approach requires only a few convolutions with small (typically 5x5) integer coefficient masks, followed by a moving-window absolute average operation.Abstract:
A method is presented for classifying each pixel of a textured image, and thus for segmenting the scene. The "texture energy" approach requires only a few convolutions with small (typically 5x5) integer coefficient masks, followed by a moving-window absolute average operation. Normalization by the local mean and standard deviation eliminates the need for histogram equalization. Rotation-invariance can also be achieved by using averages of the texture energy features. The convolution masks are separable, and can be implemented with 1-dimensional (vertical and horizontal) or multipass 3x3 convolutions. Special techniques permit rapid processing on general-purpose digital computers.read more
Citations
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Journal ArticleDOI
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
Jia-Lin Chen,A. Kundu +1 more
TL;DR: An automatic unsupervised texture segmentation scheme using hidden Markov models (HMMs) that compares favorably with respect to other successful schemes reported in the literature.
Journal ArticleDOI
Texture analysis of lesions in breast ultrasound images
TL;DR: Computerized analysis of breast US images can increase the specificity of breast sonography by providing a better characterization of solid lesions by using Haralick's texture features and posterior acoustic attenuation descriptors.
Journal ArticleDOI
SVD-Based Modeling for Image Texture Classification Using Wavelet Transformation
S. Selvan,S. Ramakrishnan +1 more
TL;DR: This paper introduces a new model for image texture classification based on wavelet transformation and singular value decomposition that achieves higher recognition rates compared to the traditional subband energy- based approach, the hybrid IMM/SVM approach, and the GGD-based approach.
Book ChapterDOI
Interest Point Detector and Feature Descriptor Survey
TL;DR: The interest point is the keypoints in each image, and often provides the scale, rotational, and illumination invariance attributes for the descriptor; the descriptor adds more detail and more invariant attributes.
Journal ArticleDOI
Multifeature texture analysis for the classification of clouds in satellite imagery
TL;DR: The aim of this work was to develop a system based on multifeature texture analysis and modular neural networks that will facilitate the automated interpretation of satellite cloud images to provide a standardized and efficient way for classifying cloud types that can be used as an operational tool in weather analysis.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
ReportDOI
Textured Image Segmentation
TL;DR: In this article, texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values, leading to a simple class of texture energy transforms, which perform better than any of the preceding methods.