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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 31–40 of 206 papers

TitleStatusHype
Latent space configuration for improved generalization in supervised autoencoder neural networks—0
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
Texture Representation via Analysis and Synthesis with Generative Adversarial Networks—0
Neutrosophic set based local binary pattern for texture classification—0
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
Texture image analysis based on joint of multi directions GLCM and local ternary patterns—0
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