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
Assessing hip osteoarthritis severity utilizing a probabilistic neural network based classification scheme.
I. Boniatis,Lena Costaridou,Dionisis Cavouras,Ioannis Kalatzis,Elias Panagiotopoulos,G.S. Panayiotakis +5 more
TL;DR: A computer-based classification system is proposed for the characterization of hips from pelvic radiographs as normal or osteoarthritic and for the discrimination among various grades of OA severity and may be valuable in OA-patient management.
Proceedings ArticleDOI
Optimal Feature Selection and Automatic Classification of Abnormal Masses in Ultrasound Liver Images
TL;DR: The possibilities of an automatic classification of ultrasonic liver images by optimal selection of texture features are explored and these features are used to classify these images into four classes-normal, cyst, benign and malignant masses.
Proceedings ArticleDOI
Texture classification using ridgelet transform
TL;DR: Features are derived from sub-bands of the ridgelet decomposition and are used for classification for a data set containing 20 texture images and Experimental results show that this approach allows to obtain a high degree of success in classification.
Dissertation
Texture and Bubble Size Measurements for Modelling Concentrate Grade in Flotation Froth Systems
Journal ArticleDOI
Ensemble selection for feature-based classification of diabetic maculopathy images
Pradeep Chowriappa,Sumeet Dua,U. Rajendra Acharya,U. Rajendra Acharya,M. Muthu Rama Krishnan +4 more
TL;DR: The objective of the proposed decision system is three fold namely, to automatically extract textural features, to effectively choose subset of discriminatory features, and to classify DM fundus images to their corresponding class of disease severity.
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.