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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 81–90 of 206 papers

TitleStatusHype
Local Rotation Invariance in 3D CNNsCode0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor—0
Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans—0
Rotation Differential Invariants of Images Generated by Two Fundamental Differential Operators—0
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters—0
Fusion of Convolutional Neural Network and Statistical Features for Texture classification—0
Spatio-spectral networks for color-texture analysisCode0
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis—0
Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?—0
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