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
Most implemented papers
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
The proposed methods are highly modular, readily plugged into existing deep CNNs.
PCANet: A Simple Deep Learning Baseline for Image Classification?
In this work, we propose a very simple deep learning network for image classification which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms.
Using Filter Banks in Convolutional Neural Networks for Texture Classification
Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself.
Wavelet Convolutional Neural Networks for Texture Classification
Based on this insight, we generalize both layers to perform a spectral analysis with wavelet transform.
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images.
Histogram Layers for Texture Analysis
GatorSense/Histogram_Layer
•
•
We present a histogram layer for artificial neural networks (ANNs).
Gray Level Co-Occurrence Matrices: Generalisation and Some New Features
Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis.
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry.
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method.
face anti-spoofing based on color texture analysis
Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones.
