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Texture Classification

Texture Classification is a fundamental issue in computer vision and image processing, playing a significant role in many applications such as medical image analysis, remote sensing, object recognition, document analysis, environment modeling, content-based image retrieval and many more.

Source: Improving Texture Categorization with Biologically Inspired Filtering

Papers

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Showing 101–110 of 206 papers

TitleStatusHype
Characterization of migrated seismic volumes using texture attributes: a comparative study—0
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral DataCode0
Scale Selective Extended Local Binary Pattern for Texture Classification—0
Completed Local Derivative Pattern for Rotation Invariant Texture Classification—0
Spatial Logics and Model Checking for Medical Imaging (Extended Version)—0
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images—0
Rotational 3D Texture Classification Using Group Equivariant CNNs—0
Porosity Amount Estimation in Stones Based on Combination of One Dimensional Local Binary Patterns and Image Normalization Technique—0
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris RecognitionCode1
Dynamic texture analysis with diffusion in networks—0
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