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000753825 020__ $$a9783319263113$$q(electronic book)
000753825 020__ $$a3319263110$$q(electronic book)
000753825 020__ $$z9783319263106
000753825 0247_ $$a10.1007/978-3-319-26311-3$$2doi
000753825 035__ $$aSP(OCoLC)ocn939404912
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000753825 1001_ $$aBrombin, Chiara,$$eauthor.
000753825 24510 $$aParametric and nonparametric inference for statistical dynamic shape analysis with applications$$h[electronic resource] /$$cChiara Brombin, Luigi Salmaso, Lara Fontanella, Luigi Ippoliti, Caterina Fusilli.
000753825 264_1 $$aCham :$$bSpringer,$$c[2016]
000753825 264_4 $$c©2016
000753825 300__ $$a1 online resource :$$billustrations.
000753825 336__ $$atext$$btxt$$2rdacontent
000753825 337__ $$acomputer$$bc$$2rdamedia
000753825 338__ $$aonline resource$$bcr$$2rdacarrier
000753825 4901_ $$aSpringerBriefs in statistics
000753825 504__ $$aIncludes bibliographical references and index.
000753825 5050_ $$aPart I Offset Normal Distribution for Dynamic Shapes -- Basic Concepts and Definitions -- Shape Inference and the Offset-Normal Distribution -- Dynamic Shape Analysis Through the Offset-Normal Distribution -- Part II Combination-Based Permutation Tests for Shape Analysis -- Parametric and Non-Parametric Testing of Mean Shapes -- Applications of NPC Methodology -- Shape Inference and the Offset-Normal Distribution. .
000753825 506__ $$aAccess limited to authorized users.
000753825 520__ $$aThis book considers specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric tests. The models are simple to understand and interpret and provide a useful tool to describe the global dynamics of the landmark configurations. However, because of the non-Euclidean nature of shape spaces, distributions in shape spaces are not straightforward to obtain. The book explores the use of the Gaussian distribution in the configuration space, with similarity transformations integrated out. Specifically, it works with the offset-normal shape distribution as a probability model for statistical inference on a sample of a temporal sequence of landmark configurations. This enables inference for Gaussian processes from configurations onto the shape space. The book is divided in two parts, with the first three chapters covering material on the offset-normal shape distribution, and the remaining chapters covering the theory of NonParametric Combination (NPC) tests. The chapters offer a collection of applications which are bound together by the theme of this book. They refer to the analysis of data from the FG-NET (Face and Gesture Recognition Research Network) database with facial expressions. For these data, it may be desirable to provide a description of the dynamics of the expressions, or testing whether there is a difference between the dynamics of two facial expressions or testing which of the landmarks are more informative in explaining the pattern of an expression.
000753825 588__ $$aOnline resource; title from PDF title page (viewed February 16, 2016).
000753825 650_0 $$aMathematical statistics.
000753825 7001_ $$aSalmaso, Luigi,$$eauthor.
000753825 7001_ $$aFontanella, Lara,$$eauthor.
000753825 7001_ $$aIppoliti, Luigi,$$eauthor.
000753825 7001_ $$aFusilli, Caterina,$$eauthor.
000753825 77608 $$iPrint version:$$z9783319263106
000753825 830_0 $$aSpringerBriefs in statistics.
000753825 852__ $$bebk
000753825 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-26311-3$$zOnline Access$$91397441.1
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