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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 41–50 of 206 papers

TitleStatusHype
A concatenating framework of shortcut convolutional neural networks—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images—0
A Machine Learning Model for Crowd Density Classification in Hajj Video Frames—0
A Theoretical Analysis of Deep Neural Networks for Texture Classification—0
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification—0
Convex Class Model on Symmetric Positive Definite Manifolds—0
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters—0
A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC—0
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis—0
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