SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 206 papers

TitleStatusHype
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation MapsCode1
C-CNN: Contourlet Convolutional Neural NetworksCode1
Histogram Layers for Texture AnalysisCode1
Wavelet Convolutional Neural NetworksCode1
Deep CNNs Meet Global Covariance Pooling: Better Representation and GeneralizationCode1
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture AnalysisCode1
Debiased Self-Training for Semi-Supervised LearningCode1
Encoding Spatial Distribution of Convolutional Features for Texture RepresentationCode1
TexTile: A Differentiable Metric for Texture TileabilityCode1
Wavelet Convolutional Neural Networks for Texture ClassificationCode1
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris RecognitionCode1
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
Amoeba Techniques for Shape and Texture Analysis—0
A concatenating framework of shortcut convolutional neural networks—0
A Machine Learning Model for Crowd Density Classification in Hajj Video Frames—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
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis—0
Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images—0
A Review on Image Texture Analysis Methods—0
A Hybrid Deep Learning Approach for Texture Analysis—0
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters—0
A Theoretical Analysis of Deep Neural Networks for Texture Classification—0
Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?—0
Show:102550
← PrevPage 1 of 9Next →

No leaderboard results yet.