Texture Classification
31 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
C-CNN: Contourlet Convolutional Neural Networks
xKHUNx/Contourlet-CNN
•
•
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.
Co-occurrence Based Texture Synthesis
As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.
3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
voreille/ssbcnn
•
•
We investigate the benefits of using the bispectrum over the spectrum in the design of a LRI layer embedded in a shallow Convolutional Neural Network (CNN) for 3D image analysis.
Local Rotation Invariance in 3D CNNs
Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations.
Histogram Layers for Texture Analysis
GatorSense/Histogram_Layer
•
•
We present a histogram layer for artificial neural networks (ANNs).
Spatio-spectral networks for color-texture analysis
Texture is one of the most-studied visual attribute for image characterization since the 1960s.
3D Geometric salient patterns analysis on 3D meshes
This paper presents a new efficient approach for geometric texture analysis on 3D triangular meshes.
Persistence Curves: A canonical framework for summarizing persistence diagrams
First, we develop a general and unifying framework of vectorizing diagrams that we call the \textit{Persistence Curves} (PCs), and show that several well-known summaries, such as Persistence Landscapes, fall under the PC framework.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
The proposed methods are highly modular, readily plugged into existing deep CNNs.
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
felixriese/CNN-SoilTextureClassification
•
•
In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.
