Journal ArticleDOI
Texture analysis using gray level run lengths
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TLDR
In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.About:
This article is published in Computer Graphics and Image Processing.The article was published on 1975-06-01. It has received 1848 citations till now. The article focuses on the topics: Image texture & Texture (geology).read more
Citations
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Journal ArticleDOI
Classification of Liver Diseases Based on Ultrasound Image Texture Features
TL;DR: Using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier is discussed.
Proceedings ArticleDOI
Automatic Facial Skin Defect Detection System
TL;DR: An automatic facial skin defect detection approach that first detects human face in the facial image, then a pattern recognition approach is applied to detect facial skin defects, such as spots and wrinkles, in the regions of interest.
Journal ArticleDOI
Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features
TL;DR: Experiments show that a small set of 12 features is able to ensure classification accuracy exceeding 90%, while all of the 183 features provide an accuracy rate of 88.94%.
Journal ArticleDOI
Computer Recognition of Tamil, Malayalam and Devanagari Characters
TL;DR: Computer recognition of multifont Tamil characters and special sets of printed Malayalam and Devanagari characters is described, where each character is converted manually into a rectangular binary array in which a zero represents a blank, and a one, a nonblank.
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.
Journal ArticleDOI
Gray-Level Manipulation Experiments for Texture Analysis
TL;DR: Some gray-level manipulation techniques are described, the first of which involves changing thegray-level distribution within the picture, and a method for extracting relatively noise-free objects from a noisy background is described.
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