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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 61–70 of 206 papers

TitleStatusHype
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
Dynamic texture analysis for detecting fake faces in video sequences—0
C-CNN: Contourlet Convolutional Neural NetworksCode1
A cellular automata approach to local patterns for texture recognition—0
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
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