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
Monitoring and grading of tea by computer vision – A review
TL;DR: An overview of computer vision based algorithms for colour and texture analysis with a special orientation towards monitoring and grading of made tea is presented in this article, where computer vision and image analysis are non-destructive procedures for sorting tea on the basis of its physical parameters viz. granule colour, shape, size and texture.
Book ChapterDOI
Distance transformations and skeletons of digitized pictures with applications
TL;DR: All aspects of variations and extensions, such as the DT of a line pattern, exoskeleton, quasi-Euclidean DT, variable neighborhood DT, and grey weighted DT are presented.
Journal ArticleDOI
Spatially quantitative seafloor habitat mapping: example from the northern South Carolina inner continental shelf
TL;DR: In this paper, the results of a spatially quantitative mapping approach based on classification of sidescan-sonar imagery were presented. But the results showed that hard bottom areas provided the geological substrate that can support diverse assemblages of sessile benthic organisms.
Journal ArticleDOI
Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization.
David Molina,Julián Pérez-Beteta,Alicia Martínez-González,Juan Martino,Carlos Velasquez,Estanislao Arana,Víctor M. Pérez-García +6 more
TL;DR: Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes, and standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies.
Journal ArticleDOI
Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
TL;DR: ML-based CT radiomics analysis can be used to predict the WHO/ISUP grade of ccRCCs preoperatively and is effective in discriminating between low grade and high grade clear cell renal cell carcinomas.
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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