Results 201 to 210 of about 125,642 (262)

Covalent Attachment of Quaternary Ammonium Group to Geopolymer Surface With Silane Coupling Agent to Enable Simultaneous Adsorption of Cations and Anions

Advanced Materials Interfaces, EarlyView.
Modification of geopolymer surface with a silane coupling agent containing quaternary ammonium group enables the development of dual‐functioning ion exchanger for both cations and anions. In the present study, the performance of this material was demonstrated for the uptake of ammonium and nitrate from water.
Yangmei Yu, Marco Fazzini, Giorgia Franchin, Paolo Colombo, Dario Frascari, Md Faysal Hossain, Priyadharshini Perumal, Tero Luukkonen   +7 more
wiley   +1 more source

Texture Features of ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography for Predicting Programmed Death-Ligand-1 Levels in Non-Small Cell Lung Cancer. [PDF]

J Clin Med
Norikane T, Ishimura M, Mitamura K, Yamamoto Y, Arai-Okuda H, Manabe Y, Murao M, Morita R, Obata T, Tanaka K, Murota M, Kanaji N, Nishiyama Y.   +12 more
europepmc   +1 more source

A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging. [PDF]

Magn Reson Imaging
Khagi B, Belousova T, Short CM, Taylor A, Nambi V, Ballantyne CM, Bismuth J, Shah DJ, Brunner G.   +8 more
europepmc   +1 more source

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Texture features based on texture spectrum

Pattern Recognition, 1991
Abstract Texture is an important spatial feature useful for identifying objects or regions of interest in an image. This paper presents a new set of textural measures derived from the texture spectrum. The proposed features extract textural information of an image with a more complete respect of texture characteristics (in all the eight directions ...
Dong-Chen He
exaly   +2 more sources

Statistical geometrical features for texture classification [PDF]

Pattern Recognition, 1995
Abstract This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray ...
Mark S Nixon, Yan Qiu Chen
exaly   +2 more sources

Texture discrimination based on an optimal utilization of texture features

Pattern Recognition, 1988
Abstract We present a new approach to texture discrimination based on an algorithm which automatically selects the texture features best suited to a particular classification problem. Promising results are obtained when applying the method to the discrimination of 10 Brodatz's features.
Dong-Chen He, Li Wang 0002, Jean Guibert
exaly   +2 more sources

Texture Browser: Feature‐based Texture Exploration

Computer Graphics Forum, 2021
AbstractTexture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult.
Xuejiao Luo, Leonardo Scandolo, Elmar Eisemann   +2 more
openaire   +1 more source

Textural features corresponding to textural properties

IEEE Transactions on Systems, Man, and Cybernetics, 1989
Five properties of texture, namely, coarseness, contrast, business, complexity, and texture strength, are given conceptual definitions in terms of spatial changes in intensity. These conceptual definitions are then approximated in computational forms.
Moses Amadasun, Robert King
openaire   +1 more source

Texture feature extraction

Pattern Recognition Letters, 1987
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He, Li Wang 0002, Jean Guibert
openaire   +1 more source

Feature extraction for texture classification

Pattern Recognition, 1980
Abstract We address the problem of texture classification. Random walks are simulated for plane domains A bounded by absorbing boundaries Γ, and the absorption distributions are estimated. Measurements derived from the above distributions are the features used for texture classification.
Harry Wechsler, Todd K. Citron
openaire   +1 more source