Texture Classification
30 papers with code • 0 benchmarks • 5 datasets
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
Benchmarks
These leaderboards are used to track progress in Texture Classification
ElBa
Multidimensional Texture Perception
ISOD
CBTex
Latest papers
TexTile: A Differentiable Metric for Texture Tileability
crp94/textile
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We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i. e., the tileability).
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
scabini/RADAM
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Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied.
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks.
Unsupervised Learning of the Total Variation Flow
Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, an unsupervised neural network approach, to approximate the solution of the TV flow given an initial image and a time instance.
Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images
Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability.
Debiased Self-Training for Semi-Supervised Learning
thuml/debiased-self-training
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Yet these datasets are time-consuming and labor-exhaustive to obtain on realistic tasks.
Encoding Spatial Distribution of Convolutional Features for Texture Representation
csfengli/fenet
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NeurIPS 2021
Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation.
Inference via Sparse Coding in a Hierarchical Vision Model
Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology.
Identifying the Origin of Finger Vein Samples Using Texture Descriptors
Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and for initiating sensor-specific processing pipelines in sensor-heterogeneous environments.
C-CNN: Contourlet Convolutional Neural Networks
xKHUNx/Contourlet-CNN
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IEEE Transactions on Neural Networks and Learning Systems 2020
Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture.
