Huckemann, Stephan F.

[["dc.bibliographiccitation.firstpage","593"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Pattern Analysis and Machine Intelligence"],["dc.bibliographiccitation.lastpage","603"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Hotz, Thomas"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:46:05Z"],["dc.date.available","2017-09-07T11:46:05Z"],["dc.date.issued","2010"],["dc.description.abstract","We propose an intrinsic multifactorial model for data on Riemannian manifolds that typically occur in the statistical analysis of shape. Due to the lack of a linear structure, linear models cannot be defined in general; to date only one-way MANOVA is available. For a general multifactorial model, we assume that variation not explained by the model is concentrated near elements defining the effects. By determining the asymptotic distributions of respective sample covariances under parallel transport, we show that they can be compared by standard MANOVA. Often in applications manifolds are only implicitly given as quotients, where the bottom space parallel transport can be expressed through a differential equation. For Kendall's space of planar shapes, we provide an explicit solution. We illustrate our method by an intrinsic two-way MANOVA for a set of leaf shapes. While biologists can identify genotype effects by sight, we can detect height effects that are otherwise not identifiable."],["dc.identifier.doi","10.1109/TPAMI.2009.117"],["dc.identifier.gro","3142944"],["dc.identifier.isi","000274548800003"],["dc.identifier.pmid","20224117"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/404"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.issn","0162-8828"],["dc.title","Intrinsic MANOVA for Riemannian Manifolds with an Application to Kendall's Space of Planar Shapes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1007"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society. Series C, Applied Statistics"],["dc.bibliographiccitation.lastpage","1027"],["dc.bibliographiccitation.volume","68"],["dc.contributor.author","Markert, Karla"],["dc.contributor.author","Krehl, Karolin"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2020-12-10T18:36:28Z"],["dc.date.available","2020-12-10T18:36:28Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1111/rssc.12343"],["dc.identifier.issn","0035-9254"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76633"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","DOI-Import GROB-394"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.title","Detecting anisotropy in fingerprint growth"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","177"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Annals of the Institute of Statistical Mathematics"],["dc.bibliographiccitation.lastpage","193"],["dc.bibliographiccitation.volume","67"],["dc.contributor.author","Hotz, Thomas"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:50:30Z"],["dc.date.available","2017-09-07T11:50:30Z"],["dc.date.issued","2015"],["dc.description.abstract","This paper gives a comprehensive treatment of local uniqueness, asymptotics and numerics for intrinsic sample means on the circle. It turns out that local uniqueness as well as rates of convergence are governed by the distribution near the antipode. If the distribution is locally less than uniform there, we have local uniqueness and asymptotic normality with a square-root rate. With increased proximity to the uniform distribution the rate can be arbitrarily slow, and in the limit, local uniqueness is lost. Further, we give general distributional conditions, e.g., unimodality, that ensure global uniqueness. Along the way, we discover that sample means can occur only at the vertices of a regular polygon which allows to compute intrinsic sample means in linear time from sorted data. This algorithm is finally applied in a simulation study demonstrating the dependence of the convergence rates on the behavior of the density at the antipode."],["dc.identifier.doi","10.1007/s10463-013-0444-7"],["dc.identifier.gro","3147630"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5093"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","0020-3157"],["dc.title","Intrinsic means on the circle: uniqueness, locus and asymptotics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","563"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series B (Statistical Methodology)"],["dc.bibliographiccitation.lastpage","587"],["dc.bibliographiccitation.volume","78"],["dc.contributor.author","Hartmann, Alexander K."],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Dannemann, Jörn"],["dc.contributor.author","Laitenberger, Oskar"],["dc.contributor.author","Geisler, Claudia"],["dc.contributor.author","Egner, Alexander"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:44:53Z"],["dc.date.available","2017-09-07T11:44:53Z"],["dc.date.issued","2016"],["dc.description.abstract","A major challenge in many modern superresolution fluorescence microscopy techniques at the nanoscale lies in the correct alignment of long sequences of sparse but spatially and temporally highly resolved images. This is caused by the temporal drift of the protein structure, e.g. due to temporal thermal inhomogeneity of the object of interest or its supporting area during the observation process. We develop a simple semiparametric model for drift correction in single-marker switching microscopy. Then we propose an M-estimator for the drift and show its asymptotic normality. This is used to correct the final image and it is shown that this purely statistical method is competitive with state of the art calibration techniques which require the incorporation of fiducial markers in the specimen. Moreover, a simple bootstrap algorithm allows us to quantify the precision of the drift estimate and its effect on the final image estimation. We argue that purely statistical drift correction is even more robust than fiducial tracking, rendering the latter superfluous in many applications. The practicability of our method is demonstrated by a simulation study and by a single-marker switching application. This serves as a prototype for many other typical imaging techniques where sparse observations with high temporal resolution are blurred by motion of the object to be reconstructed."],["dc.identifier.doi","10.1111/rssb.12128"],["dc.identifier.gro","3141679"],["dc.identifier.isi","000376150200002"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8562"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.eissn","1467-9868"],["dc.relation.issn","1369-7412"],["dc.title","Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Scandinavian Journal of Statistics"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:50:32Z"],["dc.date.available","2017-09-07T11:50:32Z"],["dc.date.issued","2011"],["dc.identifier.doi","10.1111/j.1467-9469.2010.00724.x"],["dc.identifier.gro","3147641"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5100"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Wiley-Blackwell"],["dc.relation.issn","0303-6898"],["dc.title","Inference on 3D Procrustes Means: Tree Bole Growth, Rank Deficient Diffusion Tensors and Perturbation Models"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","263"],["dc.bibliographiccitation.lastpage","282"],["dc.bibliographiccitation.seriesnr","134"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Hauke, Lara"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Rehfeldt, Florian"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.editor","Salditt, Tim"],["dc.contributor.editor","Egner, Alexander"],["dc.contributor.editor","Luke, D. Russell"],["dc.date.accessioned","2021-04-21T11:15:39Z"],["dc.date.available","2021-04-21T11:15:39Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/978-3-030-34413-9_10"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/84280"],["dc.relation","SFB 755: Nanoscale Photonic Imaging"],["dc.relation.crisseries","Topics in Applied Physics"],["dc.relation.doi","10.1007/978-3-030-34413-9"],["dc.relation.eisbn","978-3-030-34413-9"],["dc.relation.isbn","978-3-030-34412-2"],["dc.relation.ispartof","Nanoscale Photonic Imaging"],["dc.relation.ispartofseries","Topics in Applied Physics; 134"],["dc.relation.orgunit","Institut für Röntgenphysik"],["dc.subject.gro","SFB 755"],["dc.title","A Statistical and Biophysical Toolbox to Elucidate Structure and Formation of Stress Fibers"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","78"],["dc.bibliographiccitation.lastpage","82"],["dc.contributor.author","Imdahl, Christina"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.editor","Lončarić, S."],["dc.date.accessioned","2017-09-07T11:48:35Z"],["dc.date.available","2017-09-07T11:48:35Z"],["dc.date.issued","2015"],["dc.description.abstract","Synthetic fingerprint generation has two major advantages. First, it is possible to create arbitrarily large databases for research purposes e.g. of a million or a billion fingerprints at virtually no cost and without legal constraints. Secondly, together with the generated fingerprint images comes additional ground truth information for free such as e.g. the corresponding minutiae template. However, recently it has been shown that existing methods in the literature synthesize images with unrealistic minutiae configurations, usually not visible to the naked eye of an expert. In this paper, we propose an algorithm called Realistic Fingerprint Creator (RFC) for the generation realistic synthetic fingerprint images, which, as a core ingredient, involves a selection procedure how to choose the most 'realistic' synthetic fingerprints to build a database. We have performed a test of realness comparing prints synthesized by RFC and real fingerprints, and we have observed that the proposed RFC is the first method which produces artificial fingerprints that pass this test due to their realistic minutiae configuration."],["dc.identifier.doi","10.1109/ispa.2015.7306036"],["dc.identifier.gro","3146930"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4712"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway"],["dc.relation.conference","9th International Symposium on Image and Signal Processing and Analysis"],["dc.relation.eventend","2015-09-09"],["dc.relation.eventlocation","Zagreb"],["dc.relation.eventstart","2015-09-07"],["dc.relation.isbn","978-1-4673-8032-4"],["dc.relation.ispartof","ISPA 2015 : 9th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, September 7-9, 2015"],["dc.title","Towards generating realistic synthetic fingerprint images"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Electronic Journal of Probability"],["dc.bibliographiccitation.lastpage","34"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Mattingly, Jonathan"],["dc.contributor.author","Miller, Ezra"],["dc.contributor.author","Nolen, James"],["dc.date.accessioned","2017-09-07T11:48:36Z"],["dc.date.available","2017-09-07T11:48:36Z"],["dc.date.issued","2015"],["dc.identifier.doi","10.1214/ejp.v20-3887"],["dc.identifier.gro","3146932"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4714"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1083-6489"],["dc.title","Sticky central limit theorems at isolated hyperbolic planar singularities"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]