| 書(shū)目名稱 | Elastic Shape Analysis of Three-Dimensional Objects | | 編輯 | Ian H.‘Jermyn,Sebastian Kurtek,Anuj Srivastava | | 視頻video | http://file.papertrans.cn/306/305533/305533.mp4 | | 叢書(shū)名稱 | Synthesis Lectures on Computer Vision | | 圖書(shū)封面 |  | | 描述 | . Statistical analysis of shapes of 3D objects is an important problem with a wide range of applications. This analysis is difficult for many reasons, including the fact that objects differ in both geometry and topology. In this manuscript, we narrow the problem by focusing on objects with fixed topology, say objects that are diffeomorphic to unit spheres, and develop tools for analyzing their geometries. The main challenges in this problem are to register points across objects and to perform analysis while being invariant to certain shape-preserving transformations...We develop a comprehensive framework for analyzing shapes of spherical objects, i.e., objects that are embeddings of a unit sphere in #x211D;,including tools for: quantifying shape differences, optimally deforming shapes into each other, summarizing shape samples, extracting principal modes of shape variability, and modeling shape variability associated with populations. An important strength of this frameworkis that it is elastic: it performs alignment, registration, and comparison in a single unified framework, while being invariant to shape-preserving transformations...The approach is essentially Riemannian in the | | 出版日期 | Book 2017 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01819-0 | | isbn_softcover | 978-3-031-00691-3 | | isbn_ebook | 978-3-031-01819-0Series ISSN 2153-1056 Series E-ISSN 2153-1064 | | issn_series | 2153-1056 | | copyright | Springer Nature Switzerland AG 2017 |
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