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
A comprehensive review of fruit and vegetable classification techniques
TL;DR: A critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable is presented.
Journal ArticleDOI
Supervised textured image segmentation using feature smoothing and probabilistic relaxation techniques
J.Y. Hsiao,A.A. Sawchuk +1 more
TL;DR: A description is given of a supervised textured image segmentation algorithm that provides improved segmentation results based on an adaptive noise smoothing concept that takes the nonstationary nature of the problem into account.
Journal ArticleDOI
Automated diagnosis of epilepsy using CWT, HOS and texture parameters.
U. Rajendra Acharya,U. Rajendra Acharya,Ratna Yanti,Jia Wei Zheng,M. Muthu Rama Krishnan,Jen Hong Tan,Roshan Joy Martis,Choo Min Lim +7 more
TL;DR: This work proposes a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform, Higher Order Spectra and textures, and observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results.
Journal ArticleDOI
Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan™ systems
U. Rajendra Acharya,S. Vinitha Sree,M. Muthu Rama Krishnan,Filippo Molinari,Roberto Garberoglio,Jasjit S. Suri +5 more
TL;DR: A novel integrated index called Thyroid Malignancy Index (TMI) is proposed using the combination of FD, LBP, LTE texture features, to diagnose benign or malignant nodules.
Journal ArticleDOI
Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images.
TL;DR: The aim of this study was to evaluate five texture analysis techniques and determine their ability to distinguish between plaque lesions of different composition, and it was indicated that Haralick's method yielded the most accurate results.
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.