Anatomically Plausible Surface Alignment and Reconstruction

dc.contributor.authorPaulsen, Rasmus R.en_US
dc.contributor.authorLarsen, Rasmusen_US
dc.contributor.editorJohn Collomosse and Ian Grimsteaden_US
dc.date.accessioned2014-01-31T20:12:01Z
dc.date.available2014-01-31T20:12:01Z
dc.date.issued2010en_US
dc.description.abstractWith the increasing clinical use of 3D surface scanners, there is a need for accurate and reliable algorithms that can produce anatomically plausible surfaces. In this paper, a combined method for surface alignment and reconstruction is proposed. It is based on an implicit surface representation combined with a Markov Random Field regularisation method. Conceptually, the method maintains an implicit ideal description of the sought surface. This implicit surface is iteratively updated by realigning the input point sets and Markov Random Field regularisation. The regularisation is based on a prior energy that has earlier proved to be particularly well suited for human surface scans. The method has been tested on full cranial scans of ten test subjects and on several scans of the outer human ear.en_US
dc.description.seriesinformationTheory and Practice of Computer Graphicsen_US
dc.identifier.isbn978-3-905673-75-3en_US
dc.identifier.urihttps://doi.org/10.2312/LocalChapterEvents/TPCG/TPCG10/249-254en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Line and Curve Generation I.4.8 [Image Processing and Computer Vision]: Surface fittingen_US
dc.titleAnatomically Plausible Surface Alignment and Reconstructionen_US
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