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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 121–130 of 206 papers

TitleStatusHype
Improving LBP and its variants using anisotropic diffusion—0
Improving Texture Categorization with Biologically Inspired Filtering—0
Improving weather radar by fusion and classification—0
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering—0
Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5—0
Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens—0
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices—0
Lacunarity Analysis on Image Patterns for Texture Classification—0
Large-Margin Representation Learning for Texture Classification—0
Latent space configuration for improved generalization in supervised autoencoder neural networks—0
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