LEADER 04820nam 22006735 450 001 9910254080203321 005 20220330185739.0 010 $a3-319-26311-0 024 7 $a10.1007/978-3-319-26311-3 035 $a(CKB)3710000000596639 035 $a(EBL)4405865 035 $a(SSID)ssj0001653742 035 $a(PQKBManifestationID)16433038 035 $a(PQKBTitleCode)TC0001653742 035 $a(PQKBWorkID)14982769 035 $a(PQKB)10826817 035 $a(DE-He213)978-3-319-26311-3 035 $a(MiAaPQ)EBC4405865 035 $a(PPN)19221991X 035 $a(EXLCZ)993710000000596639 100 $a20160211d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aParametric and nonparametric inference for statistical dynamic shape analysis with applications /$fby Chiara Brombin, Luigi Salmaso, Lara Fontanella, Luigi Ippoliti, Caterina Fusilli 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (120 p.) 225 1 $aSpringerBriefs in Statistics,$x2191-544X 300 $aDescription based upon print version of record. 311 $a3-319-26310-2 320 $aIncludes bibliographical references and index. 327 $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. . 330 $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. 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