Journal Article10.1016/J.PATREC.2013.02.009
Texture databases - A comprehensive survey
Shahera Hossain,Seiichi Serikawa +1 more
70
TL;DR: This elegant survey categorize and critically survey based on many references of the state-of-the-art related to the databases and other texture works so that it becomes helpful for a researcher to choose and evaluate having crucial evaluating aspects in mind.
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About: This article is published in Pattern Recognition Letters. The article was published on 01 Nov 2013. The article focuses on the topics: Field (computer science).
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Citations
Multi-class texture analysis in colorectal cancer histology
Jakob Nikolas Kather,Cleo Aron Weis,Francesco Bianconi,Susanne Melchers,Lothar R. Schad,Timo Gaiser,Alexander Marx,Frank G. Zöllner +7 more
TL;DR: A new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue is presented and an optimal classification strategy is found that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation and setting a new standard for multiclass tissue separation.
558
Texture Feature Extraction Methods: A Survey
TL;DR: This survey provides a comprehensive survey of the texture feature extraction methods and identifies two classes of methods that deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.
Deep learning for biological image classification
TL;DR: This paper investigates the classification of the quality of wood boards based on their images with the use of deep learning, particularly Convolutional Neural Networks, with the combination of texture-based feature extraction techniques and traditional techniques: Decision tree induction algorithms, Neural networks, Nearest neighbors and Support vector machines.
273
Evaluating color texture descriptors under large variations of controlled lighting conditions.
TL;DR: An extensive comparison of old and new texture features, with and without a color normalization step, is reported, with a particular focus on how these features are affected by small and large variations in the lighting conditions.
72
Visibility Graphs for Image Processing
Jacopo Iacovacci,Lucas Lacasa +1 more
TL;DR: In this paper, the authors explore the usefulness of image visibility graphs in the scenario of image processing and image classification and demonstrate that the link architecture of the image visibility graph encapsulates relevant information on the structure of the images and explore their potential as image filters.
References
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study
Jianguo Zhang,Marcin Marszalek,Svetlana Lazebnik,Cordelia Schmid +3 more
- 17 Jun 2006
TL;DR: A large-scale evaluation of an approach that represents images as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ2 distance.
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
Thomas Leung,Jitendra Malik +1 more
TL;DR: A unified model to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions is provided.
1.8K
Reflectance and texture of real-world surfaces
TL;DR: A new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction is discussed, and a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions is presented.
A sparse texture representation using local affine regions
TL;DR: The proposed texture representation is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.