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
Most implemented papers
Learning rotation invariant convolutional filters for texture classification
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN).
FWLBP: A Scale Invariant Descriptor for Texture Classification
In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary Pattern (FWLBP).
Wavelet Convolutional Neural Networks
Given that spatial and spectral approaches are known to have different characteristics, it will be interesting to incorporate a spectral approach into CNNs.
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.
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.
3D Geometric salient patterns analysis on 3D meshes
This paper presents a new efficient approach for geometric texture analysis on 3D triangular meshes.
Spatio-spectral networks for color-texture analysis
Texture is one of the most-studied visual attribute for image characterization since the 1960s.
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.
3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
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.
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.