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

Most implemented papers

PCANet: A Simple Deep Learning Baseline for Image Classification?

Recognito-Vision/NIST-FRVT-Top-1-Face-Recognition 14 Apr 2014

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.

Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

jiangtaoxie/fast-MPN-COV 15 Apr 2019

The proposed methods are highly modular, readily plugged into existing deep CNNs.

Using Filter Banks in Convolutional Neural Networks for Texture Classification

v-andrearczyk/caffe-TCNN 12 Jan 2016

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

menon92/WaveletCNN 24 Jul 2017

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

CVRL/domain-specific-BSIF-for-iris-recognition 13 Jul 2018

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 1 Jan 2020

We present a histogram layer for artificial neural networks (ANNs).

Gray Level Co-Occurrence Matrices: Generalisation and Some New Features

adipai/haralick-textural-feature-analysis 22 May 2012

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

chengcv/J3S 16 Apr 2013

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

Lukeli0425/Fire-Detection 10 Jun 2015

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

coderwangson/Face-anti-spoofing-based-on-color-texture-analysis 19 Nov 2015

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.