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Texture Classification

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

Papers

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Showing 21–30 of 206 papers

TitleStatusHype
A Review on Image Texture Analysis Methods—0
A Hybrid Deep Learning Approach for Texture Analysis—0
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters—0
A Theoretical Analysis of Deep Neural Networks for Texture Classification—0
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform—0
Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis—0
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs—0
Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?—0
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