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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CVRL/domain-specific-BSIF-for-iris-recognition
공식 코드Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
https://github.com/GatorSense/Histogram_Layer
공식 코드https://github.com/ZhangLi-CS/GCP_Optimization
공식 코드Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
https://github.com/advanced-vision-and-learning-lab/2024_v4a_lacunarity_pooling_layer
공식 코드Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis
https://github.com/advanced-vision-and-learning-lab/passive_sonar_texture_analysis
공식 코드https://github.com/azlawson/PersistenceCurves
공식 코드Persistence Curves: A canonical framework for summarizing persistence diagrams
https://github.com/coocgan/cooc_texture
공식 코드https://github.com/crp94/textile
공식 코드https://github.com/deepmind/dm_nevis
공식 코드NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
https://github.com/felixriese/CNN-SoilTextureClassification
공식 코드Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
https://github.com/haail/glog-csunet
공식 코드https://github.com/jiangtaoxie/MPN-COV
공식 코드Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
https://github.com/jiangtaoxie/fast-MPN-COV
공식 코드Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
https://github.com/scabini/RADAM
공식 코드RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
https://github.com/scabini/VORTEX
공식 코드https://github.com/scabini/ssn
공식 코드https://github.com/tamaragrossmann/tvflownet
공식 코드https://github.com/tejaspanambur/mastcam
공식 코드Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images
https://github.com/thuml/debiased-self-training
공식 코드https://github.com/v-andrearczyk/caffe-TCNN
공식 코드Using Filter Banks in Convolutional Neural Networks for Texture Classification
https://github.com/v-andrearczyk/lri-cnn
공식 코드https://github.com/voreille/ssbcnn
공식 코드3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
https://notabug.org/jbowren/hv2model
공식 코드https://github.com/Ldpe2G/PCANet
PCANet: A Simple Deep Learning Baseline for Image Classification?
https://github.com/Lukeli0425/Fire-Detection
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
https://github.com/Shantanu48114860/MTL-TCNN3
Using Filter Banks in Convolutional Neural Networks for Texture Classification
https://github.com/aczajka/iris-recognition---pm-diseased-human-driven-bsif
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
https://github.com/adipai/haralick-textural-feature-analysis
Gray Level Co-Occurrence Matrices: Generalisation and Some New Features
https://github.com/chengcv/J3S
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
https://github.com/coderwangson/Face-anti-spoofing-based-on-color-texture-analysis
https://github.com/ladislasl/CNN_invar_rot
Learning rotation invariant convolutional filters for texture classification
https://github.com/lucasleesw/PCANet_python_ver
PCANet: A Simple Deep Learning Baseline for Image Classification?
https://github.com/menon92/WaveletCNN
Wavelet Convolutional Neural Networks for Texture Classification
https://github.com/securade/hub
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
https://github.com/shinfj/WaveletCNN_for_TextureClassification
Wavelet Convolutional Neural Networks for Texture Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.