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.artnumber","12"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","EURASIP Journal on Image and Video Processing"],["dc.bibliographiccitation.volume","2016"],["dc.contributor.author","Thai, D. H."],["dc.contributor.author","Gottschlich, C."],["dc.date.accessioned","2019-07-09T11:43:11Z"],["dc.date.available","2019-07-09T11:43:11Z"],["dc.date.issued","2016"],["dc.description.abstract","We consider the task of image decomposition, and we introduce a new model coined directional global three-part decomposition (DG3PD) for solving it. As key ingredients of the DG3PD model, we introduce a discrete multi-directional total variation norm and a discrete multi-directional G-norm. Using these novel norms, the proposed discrete DG3PD model can decompose an image into two or three parts. Existing models for image decomposition by Vese and Osher (J. Sci. Comput. 19(1–3):553–572, 2003), by Aujol and Chambolle (Int. J. Comput. Vis. 63(1):85–104, 2005), by Starck et al. (IEEE Trans. Image Process. 14(10):1570–1582, 2005), and by Thai and Gottschlich are included as special cases in the new model. Decomposition of an image by DG3PD results in a cartoon image, a texture image, and a residual image. Advantages of the DG3PD model over existing ones lie in the properties enforced on the cartoon and texture images. The geometric objects in the cartoon image have a very smooth surface and sharp edges. The texture image yields oscillating patterns on a defined scale which are both smooth and sparse. Moreover, the DG3PD method achieves the goal of perfect reconstruction by summation of all components better than the other considered methods. Relevant applications of DG3PD are a novel way of image compression as well as feature extraction for applications such as latent fingerprint processing and optical character recognition."],["dc.identifier.doi","10.1186/s13640-016-0110-0"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14310"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58839"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1687-5281"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Directional global three-part image decomposition"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","802"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Information Forensics and Security"],["dc.bibliographiccitation.lastpage","811"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Mihailescu, Preda"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:46:45Z"],["dc.date.available","2017-09-07T11:46:45Z"],["dc.date.issued","2009"],["dc.description.abstract","Orientation field ( OF) estimation is a crucial preprocessing step in fingerprint image processing. In this paper, we present a novel method for OF estimation that uses traced ridge and valley lines. This approach provides robustness against disturbances caused, e. g., by scars, contamination, moisture, or dryness of the finger. It considers pieces of flow information from a larger region and makes good use of fingerprint inherent properties like continuity of ridge flow perpendicular to the flow. The performance of the line-sensor method is compared with the gradients-based method and a multiscale directional operator. Its robustness is tested in experiments with simulated scar noise which is drawn on top of good quality fingerprint images from the FVC2000 and FVC2002 databases. Finally, the effectiveness of the line-sensor-based approach is demonstrated on 60 naturally poor quality fingerprint images from the FVC2004 database. All orientations marked by a human expert are made available at the journal's and the authors' website for comparative tests."],["dc.identifier.doi","10.1109/TIFS.2009.2033219"],["dc.identifier.gro","3143017"],["dc.identifier.isi","000271968200005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/485"],["dc.notes.intern","WoS Import 2017-03-10 / Funder: DFG [FOR 916]; Volkswagen Foundation"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1556-6013"],["dc.title","Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2220"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Image Processing"],["dc.bibliographiccitation.lastpage","2227"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2018-11-07T09:11:40Z"],["dc.date.available","2018-11-07T09:11:40Z"],["dc.date.issued","2012"],["dc.description.abstract","Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods."],["dc.description.sponsorship","DFG RTG [1023]"],["dc.identifier.doi","10.1109/TIP.2011.2170696"],["dc.identifier.isi","000302181800064"],["dc.identifier.pmid","21984503"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/26774"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1941-0042"],["dc.relation.issn","1057-7149"],["dc.title","Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","271"],["dc.bibliographiccitation.journal","IEEE Access"],["dc.bibliographiccitation.lastpage","282"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Schrieber, Joern"],["dc.contributor.author","Schuhmacher, Dominic"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2018-11-07T10:29:01Z"],["dc.date.available","2018-11-07T10:29:01Z"],["dc.date.issued","2017"],["dc.description.abstract","The Wasserstein metric or earth mover's distance is a useful tool in statistics, computer science and engineering with many applications to biological or medical imaging, among others. Especially in the light of increasingly complex data, the computation of these distances via optimal transport is often the limiting factor. Inspired by this challenge, a variety of new approaches to optimal transport has been proposed in recent years and along with these new methods comes the need for a meaningful comparison. In this paper, we introduce a benchmark for discrete optimal transport, called DOTmark, which is designed to serve as a neutral collection of problems, where discrete optimal transport methods can be tested, compared with one another, and brought to their limits on large-scale instances. It consists of a variety of grayscale images, in various resolutions and classes, such as several types of randomly generated images, classical test images and real data from microscopy. Along with the DOTmark we present a survey and a performance test for a cross section of established methods ranging from more traditional algorithms, such as the transportation simplex, to recently developed approaches, such as the shielding neighborhood method, and including also a comparison with commercial solvers."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2016"],["dc.identifier.doi","10.1109/ACCESS.2016.2639065"],["dc.identifier.gro","3145880"],["dc.identifier.isi","000396132600013"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14253"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43551"],["dc.notes.intern","mathe"],["dc.notes.intern","DOI-Import GROB-394"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Institute of Electrical and Electronics Engineers (IEEE)"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.relation.eissn","2169-3536"],["dc.relation.issn","2169-3536"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0/"],["dc.subject","earth mover’s distance Optimal transport benchmark Wasserstein metric Benchmark testing Image resolution Microscopy Extraterrestrial measurements Shape Transportation"],["dc.title","DOTmark - A Benchmark for Discrete Optimal Transport"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e110214"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Schuhmacher, Dominic"],["dc.date.accessioned","2017-09-07T11:50:30Z"],["dc.date.available","2017-09-07T11:50:30Z"],["dc.date.issued","2014"],["dc.description.abstract","Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.identifier.doi","10.1371/journal.pone.0110214"],["dc.identifier.gro","3145883"],["dc.identifier.pmid","25310106"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10992"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3616"],["dc.notes.intern","mathe"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Public Library of Science (PLoS)"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0/"],["dc.subject","Transportation Algorithms Computer and information sciences Consortia Optimization Approximation methods Imaging techniques Simulation and modeling"],["dc.title","The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]