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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

Showing 101125 of 206 papers

TitleStatusHype
Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images0
Segmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analysis0
Provably scale-covariant networks from oriented quasi quadrature measures in cascade0
Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study0
Characterization of migrated seismic volumes using texture attributes: a comparative study0
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral DataCode0
Scale Selective Extended Local Binary Pattern for Texture Classification0
Completed Local Derivative Pattern for Rotation Invariant Texture Classification0
Spatial Logics and Model Checking for Medical Imaging (Extended Version)0
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images0
Rotational 3D Texture Classification Using Group Equivariant CNNs0
Porosity Amount Estimation in Stones Based on Combination of One Dimensional Local Binary Patterns and Image Normalization Technique0
Dynamic texture analysis with diffusion in networks0
Fusion of complex networks and randomized neural networks for texture analysis0
Convex Class Model on Symmetric Positive Definite Manifolds0
Fast Rotational Sparse Coding0
Classification-Driven Dynamic Image Enhancement0
Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval0
Multilayer Complex Network Descriptors for Color-Texture Characterization0
Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks0
A Review on Image Texture Analysis Methods0
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering0
Fast and accurate computation of orthogonal moments for texture analysis0
Texture Classification in Extreme Scale Variations using GANet0
Texture Segmentation Based Video Compression Using Convolutional Neural Networks0
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