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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 176–200 of 206 papers

TitleStatusHype
Spatio-temporal encoding improves neuromorphic tactile texture classification—0
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images—0
StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction—0
Supervised Dictionary Learning—0
Supervised Texture Classification Using a Novel Compression-Based Similarity Measure—0
Texture analysis by multi-resolution fractal descriptors—0
Texture analysis using deterministic partially self-avoiding walk with thresholds—0
Texture analysis using volume-radius fractal dimension—0
Texture-aware Video Frame Interpolation—0
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image SegmentationCode0
Lacunarity Pooling Layers for Plant Image Classification using Texture AnalysisCode0
Gray Level Co-Occurrence Matrices: Generalisation and Some New FeaturesCode0
Using Filter Banks in Convolutional Neural Networks for Texture ClassificationCode0
3D Geometric salient patterns analysis on 3D meshesCode0
Co-occurrence Based Texture SynthesisCode0
FWLBP: A Scale Invariant Descriptor for Texture ClassificationCode0
Learning rotation invariant convolutional filters for texture classificationCode0
face anti-spoofing based on color texture analysisCode0
Spatio-spectral networks for color-texture analysisCode0
Quantitative Measures for Passive Sonar Texture AnalysisCode0
Enhanced Wavelet Scattering Network for image inpainting detectionCode0
Local Rotation Invariance in 3D CNNsCode0
PCANet: A Simple Deep Learning Baseline for Image Classification?Code0
Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain ImagesCode0
VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token EncodingsCode0
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