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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
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
A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection—0
Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm—0
3D Geometric salient patterns analysis on 3D meshesCode0
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results—0
Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade—0
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