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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 181–190 of 206 papers

TitleStatusHype
A Hybrid Deep Learning Approach for Texture Analysis—0
A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC—0
A Machine Learning Model for Crowd Density Classification in Hajj Video Frames—0
Amoeba Techniques for Shape and Texture Analysis—0
An accurate detection of micro-collapse during the lyophilisation of a 5% w/v lactose solution using a combination of novel techniques: intelligent laser speckle imaging (ILSI) and through-vial impedance spectroscopy (TVIS)—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection—0
A non-extensive entropy feature and its application to texture classification—0
A PCA-Based Convolutional Network—0
A probabilistic patch based image representation using Conditional Random Field model for image classification—0
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