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 with no code
Neutrosophic set based local binary pattern for texture classification
As a result, using neutrosophic truth and false sets with a grayscale input image has resulted in more robust features.
PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction
We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR.
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks
Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production.
Texture image analysis based on joint of multi directions GLCM and local ternary patterns
In this article, a new approach is proposed based on combination of two efficient texture descriptor, co-occurrence matrix and local ternary patterns (LTP).
Multilayer deep feature extraction for visual texture recognition
The reason for using features from earlier convolutional layers is obtaining information that is less domain specific.
Texture features in medical image analysis: a survey
The texture is defined as spatial structure of the intensities of the pixels in an image that is repeated periodically in the whole image or regions, and makes the concept of the image.
Large-Margin Representation Learning for Texture Classification
The core of such an approach is a loss function that computes the distances between instances of interest and support vectors.
Can autism be diagnosed with AI?
With AI, new radiomic models using the deep learning techniques will be also described.
2-d signature of images and texture classification
We introduce a proper notion of 2-dimensional signature for images.
Multiscale Analysis for Improving Texture Classification
Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales.
