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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
Riesz feature representation: scale equivariant scattering network for classification tasks—0
Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5—0
Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation—0
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients—0
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation MapsCode1
Texture Representation via Analysis and Synthesis with Generative Adversarial Networks—0
Neutrosophic set based local binary pattern for texture classification—0
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
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