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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 11–20 of 206 papers

TitleStatusHype
An accurate detection of micro-collapse during the lyophilisation of a 5% w/v lactose solution using a combination of novel techniques: intelligent laser speckle imaging (ILSI) and through-vial impedance spectroscopy (TVIS)—0
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
Lacunarity Pooling Layers for Plant Image Classification using Texture AnalysisCode0
StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction—0
Compositional Neural Textures—0
Texture Classification Network Integrating Adaptive Wavelet Transform—0
TexTile: A Differentiable Metric for Texture TileabilityCode1
Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis—0
Latent space configuration for improved generalization in supervised autoencoder neural networks—0
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