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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 141–150 of 206 papers

TitleStatusHype
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification—0
Texture segmentation with Fully Convolutional Networks—0
Improving LBP and its variants using anisotropic diffusion—0
Texture Classification of MR Images of the Brain in ALS using CoHOG—0
Texture Characterization by Using Shape Co-occurrence Patterns—0
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling—0
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification—0
Multivariate mixture model for myocardium segmentation combining multi-source images—0
The Mehler-Fock Transform and some Applications in Texture Analysis and Color Processing—0
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis—0
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