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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 1–10 of 206 papers

TitleStatusHype
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation MapsCode1
Deep CNNs Meet Global Covariance Pooling: Better Representation and GeneralizationCode1
Histogram Layers for Texture AnalysisCode1
Encoding Spatial Distribution of Convolutional Features for Texture RepresentationCode1
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris RecognitionCode1
C-CNN: Contourlet Convolutional Neural NetworksCode1
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture AnalysisCode1
Debiased Self-Training for Semi-Supervised LearningCode1
TexTile: A Differentiable Metric for Texture TileabilityCode1
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