Gottschlich, Carsten

[["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","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"]]

[["dc.bibliographiccitation.artnumber","e0154160"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.lastpage","31"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Thai, Duy Hoang"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2017-09-07T11:50:29Z"],["dc.date.available","2017-09-07T11:50:29Z"],["dc.date.issued","2016"],["dc.description.abstract","Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available."],["dc.identifier.doi","10.1371/journal.pone.0154160"],["dc.identifier.gro","3147621"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13338"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5091"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Filter Design and Performance Evaluation for Fingerprint Image Segmentation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","6"],["dc.contributor.author","Oehlmann, Lars"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2017-09-07T11:48:36Z"],["dc.date.available","2017-09-07T11:48:36Z"],["dc.date.issued","2015"],["dc.description.abstract","Orientation fields (OFs) are a key element of fingerprint recognition systems. They are a requirement for important processing steps such as image enhancement by contextual filtering, and typically, they are estimated from fingerprint images. If information about a fingerprint is available only in form of a stored minutiae template, an OF can be reconstructed from this template up to a certain degree of accuracy. The reconstructed OF can then be used e.g. for fingerprint alignment or as a feature for matching, and thus, for improving directly or indirectly the recognition performance of a system. This study compares reconstruction methods from the literature on a benchmark with ground truth orientation fields. The performance of these methods is evaluated using three metrics measuring the amount of reconstruction errors as well as in terms of computational runtime."],["dc.identifier.doi","10.1109/iwbf.2015.7110237"],["dc.identifier.gro","3146931"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4713"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway, NJ"],["dc.relation.conference","International Workshop on Biometrics and Forensics"],["dc.relation.eventend","2015-03-05"],["dc.relation.eventlocation","Gjøvik, Norway"],["dc.relation.eventstart","2015-03-03"],["dc.relation.isbn","978-1-4799-8105-2"],["dc.relation.ispartof","3rd International Workshop on Biometrics and Forensics (IWBF2015)"],["dc.title","Performance evaluation of fingerprint orientation field reconstruction methods"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","183"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IET Biometrics"],["dc.bibliographiccitation.lastpage","190"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Tams, Benjamin"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:48:37Z"],["dc.date.available","2017-09-07T11:48:37Z"],["dc.date.issued","2016"],["dc.description.abstract","Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this study, a novel approach is presented to globally model an OF combined with locally adaptive methods. The authors show that this model adapts perfectly to the ‘true OF’ in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high-fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution, they describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and they establish the main underlying claim of high-fidelity low parameter OF compression."],["dc.identifier.doi","10.1049/iet-bmt.2016.0087"],["dc.identifier.gro","3146922"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4708"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","2047-4938"],["dc.title","Perfect fingerprint orientation fields by locally adaptive global models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","291"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IET Biometrics"],["dc.bibliographiccitation.lastpage","301"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:50:30Z"],["dc.date.available","2017-09-07T11:50:30Z"],["dc.date.issued","2014"],["dc.description.abstract","In this study, the authors show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. They propose a non-parametric distribution-based method using second-order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This ‘test of realness’ can be applied to synthetic fingerprints produced by any method. In this study, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. In addition, they evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz against real fingerprint images. They conclude with suggestions for the improvement of synthetic fingerprint generation."],["dc.identifier.doi","10.1049/iet-bmt.2013.0065"],["dc.identifier.gro","3147637"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5095"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","2047-4938"],["dc.title","Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0126346"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Rehfeldt, Florian"],["dc.date.accessioned","2017-09-07T11:48:37Z"],["dc.date.available","2017-09-07T11:48:37Z"],["dc.date.issued","2015"],["dc.description.abstract","A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0126346"],["dc.identifier.gro","3146928"],["dc.identifier.pmid","25996921"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4710"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1963"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","IEEE Transactions on Information Forensics and Security"],["dc.bibliographiccitation.lastpage","1974"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Richter, Robin"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Mentch, Lucas"],["dc.contributor.author","Thai, Duy H."],["dc.contributor.author","Huckemann, Stephan F."],["dc.date.accessioned","2020-12-10T18:26:17Z"],["dc.date.available","2020-12-10T18:26:17Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1109/TIFS.2018.2889258"],["dc.identifier.eissn","1556-6021"],["dc.identifier.issn","1556-6013"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76025"],["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","Smudge Noise for Quality Estimation of Fingerprints and its Validation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","651"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Mathematical Imaging and Vision"],["dc.bibliographiccitation.lastpage","660"],["dc.bibliographiccitation.volume","60"],["dc.contributor.author","Imdahl, Christina"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Ohshika, Ken’ichi"],["dc.date.accessioned","2020-12-10T14:11:39Z"],["dc.date.available","2020-12-10T14:11:39Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1007/s10851-017-0780-y"],["dc.identifier.eissn","1573-7683"],["dc.identifier.issn","0924-9907"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71150"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Möbius Moduli for Fingerprint Orientation Fields"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Journal of Mathematical Biology"],["dc.contributor.author","Düring, Bertram"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Kreusser, Lisa Maria"],["dc.contributor.author","Schönlieb, Carola-Bibiane"],["dc.date.accessioned","2019-07-09T11:51:04Z"],["dc.date.available","2019-07-09T11:51:04Z"],["dc.date.issued","2019"],["dc.description.abstract","Evidence suggests that both the interaction of so-called Merkel cells and the epidermal stress distribution play an important role in the formation of fingerprint patterns during pregnancy. To model the formation of fingerprint patterns in a biologically meaningful way these patterns have to become stationary. For the creation of synthetic fingerprints it is also very desirable that rescaling the model parameters leads to rescaled distances between the stationary fingerprint ridges. Based on these observations, as well as the model introduced by Kücken and Champod we propose a new model for the formation of fingerprint patterns during pregnancy. In this anisotropic interaction model the interaction forces not only depend on the distance vector between the cells and the model parameters, but additionally on an underlying tensor field, representing a stress field. This dependence on the tensor field leads to complex, anisotropic patterns. We study the resulting stationary patterns both analytically and numerically. In particular, we show that fingerprint patterns can be modeled as stationary solutions by choosing the underlying tensor field appropriately."],["dc.identifier.doi","10.1007/s00285-019-01338-3"],["dc.identifier.pmid","30830268"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16041"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59868"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1432-1416"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","510"],["dc.title","An anisotropic interaction model for simulating fingerprints"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]