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Texture Classification
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fast Rotational Sparse Coding
Wavelet Convolutional Neural Networks
Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval
Multilayer Complex Network Descriptors for Color-Texture Characterization
Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
A Review on Image Texture Analysis Methods
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering
Fast and accurate computation of orthogonal moments for texture analysis
Texture Classification in Extreme Scale Variations using GANet
Texture Segmentation Based Video Compression Using Convolutional Neural Networks
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
FWLBP: A Scale Invariant Descriptor for Texture Classification
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis
Human activity recognition from mobile inertial sensors using recurrence plots
Local Jet Pattern: A Robust Descriptor for Texture Classification
Deep Local Binary Patterns
Classification Driven Dynamic Image Enhancement
A concatenating framework of shortcut convolutional neural networks
Visualizing and Improving Scattering Networks
e-Counterfeit: a mobile-server platform for document counterfeit detection
Wavelet Convolutional Neural Networks for Texture Classification
Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication
A Gabor Filter Texture Analysis Approach for Histopathological Brain Tumor Subtype Discrimination
A Hybrid Deep Learning Approach for Texture Analysis
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Texture segmentation with Fully Convolutional Networks
Improving LBP and its variants using anisotropic diffusion
Texture Classification of MR Images of the Brain in ALS using CoHOG
Texture Characterization by Using Shape Co-occurrence Patterns
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification
Multivariate mixture model for myocardium segmentation combining multi-source images
The Mehler-Fock Transform and some Applications in Texture Analysis and Color Processing
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
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