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Titlebook: Discrete Geometry for Computer Imagery; 11th International C Ingela Nystr?m,Gabriella Sanniti di Baja,Stina Sve Conference proceedings 2003

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發(fā)表于 2025-3-21 18:32:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Discrete Geometry for Computer Imagery
副標題11th International C
編輯Ingela Nystr?m,Gabriella Sanniti di Baja,Stina Sve
視頻videohttp://file.papertrans.cn/282/281128/281128.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discrete Geometry for Computer Imagery; 11th International C Ingela Nystr?m,Gabriella Sanniti di Baja,Stina Sve Conference proceedings 2003
出版日期Conference proceedings 2003
關鍵詞algorithmic geometry; computational geometry; computer; computer graphics; computer imagery; computer vis
版次1
doihttps://doi.org/10.1007/b94107
isbn_softcover978-3-540-20499-2
isbn_ebook978-3-540-39966-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Germany, part of Springer Nature 2003
The information of publication is updating

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Discrete Geometry for Computer Imagery978-3-540-39966-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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0302-9743 Overview: Includes supplementary material: 978-3-540-20499-2978-3-540-39966-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Christine Autenriethe a control parameter that moves the model between displaying and not displaying spontaneous phase separation for some densities. Those phases can be called “l(fā)aminar” and “jammed”. Models with spontaneous phase separation show three states as a function of density: a first state at low density, wher
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Blind Image Forgery Detection Using LBP and?Statistical Momentsres are retrieved using a 2-level DWT. For training and testing, an ensemble classifier is utilized and further classifies the images in authentic or forged. The suggested model provides high detection accuracy with relatively low dimensionality and testing time which demonstrates its efficacy.
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Modelling Times Between Events with a Cured Fraction Using a First Hitting Time Regression Model with Individual Random Effectsng to occur at every intermediate stage. Issues of model selection are also considered. Finally, the proposed FHT regression model is fitted to a dataset consisting of the times of repeated applications for treatment made by drug users.
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