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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 71–80 of 206 papers

TitleStatusHype
Learning Local Complex Features using Randomized Neural Networks for Texture Analysis—0
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model—0
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning—0
Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung—0
Co-occurrence Based Texture SynthesisCode0
Modal features for image texture classification—0
3D Solid Spherical Bispectrum CNNs for Biomedical Texture AnalysisCode0
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images—0
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology—0
Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens—0
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