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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 91–100 of 206 papers

TitleStatusHype
Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis—0
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning—0
Heterogeneous patterns enhancing static and dynamic texture classification—0
Dynamic texture analysis with diffusion in networks—0
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
Identifying the Origin of Finger Vein Samples Using Texture Descriptors—0
Rotation Differential Invariants of Images Generated by Two Fundamental Differential Operators—0
Improved texture image classification through the use of a corrosion-inspired cellular automaton—0
Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study—0
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