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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
Completed Local Derivative Pattern for Rotation Invariant Texture Classification—0
Dynamic texture analysis for detecting fake faces in video sequences—0
Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images—0
Combined statistical and model based texture features for improved image classification—0
Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm—0
A Review on Image Texture Analysis Methods—0
A Hybrid Deep Learning Approach for Texture Analysis—0
CN-LBP: Complex Networks-based Local Binary Patterns for Texture Classification—0
Clustering Images by Unmasking - A New Baseline—0
Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?—0
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