Baum, Marcus

[["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Advances in Information Fusion"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Granström, Karl"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Reuter, Stephan"],["dc.date.accessioned","2019-07-10T08:12:29Z"],["dc.date.available","2019-07-10T08:12:29Z"],["dc.date.issued","2017"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15221"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60934"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.issn","1557-6418"],["dc.rights.access","openAccess"],["dc.subject","Extended Object Tracking; Multiple Target Tracking"],["dc.subject.ddc","510"],["dc.title","Extended Object Tracking: Introduction, Overview, and Applications"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2516"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","The American Journal of Sports Medicine"],["dc.bibliographiccitation.lastpage","2521"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Spahn, G."],["dc.contributor.author","Klinger, H. M."],["dc.contributor.author","Baums, M."],["dc.contributor.author","Hoffmann, M."],["dc.contributor.author","Plettenberg, H."],["dc.contributor.author","Kroker, A."],["dc.contributor.author","Hofmann, Gunther O."],["dc.date.accessioned","2019-07-09T11:52:57Z"],["dc.date.available","2019-07-09T11:52:57Z"],["dc.date.issued","2010"],["dc.description.abstract","Background: Mechanical tests to grade cartilage damage are limited by the instruments used and by the ability to access all areas of cartilage within a joint. Better methods to diagnose cartilage injury or degeneration are needed. Purpose/Hypothesis: To detect the interobserver variance of arthroscopic cartilage grading by subjective judgment using the International Cartilage Repair Society (ICRS) score and by objective measurement using near-infrared (NIR) spectroscopy. We hypothesized that objective measurement of cartilage lesions by NIR spectroscopy will yield more valid results than routine grading using the ICRS score. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Fifteen patients undergoing arthroscopic knee operations were evaluated by 4 experienced arthroscopists independently. The cartilage lesions within the medial knee compartment were estimated by each observer using the ICRS grade and by measurements with a special arthroscopic NIR spectroscopy probe. Results: The ICRS grading had a poor interobserver agreement, with a mean Fleiss kappa index of k = 0.173. Only in 10% (6 of 60) of judged cartilage areas did all 4 surgeons grade the cartilage areas with the same result. In 17 areas (28.3%), the surgeons had a variance of 2 or more grades. In the remaining cases, the surgeons varied within 1 grade. The objective NIR spectroscopyobtained measurements of cartilage resulted in a significant correlation within the observers of R = 0.885 6 0.036 (P\\.001). Conclusion: Our results of interobserver evaluation in real-time arthroscopic cartilage grading suggest that this subjective grading is not satisfactory. This study emphasizes the need for objective measurement techniques for arthroscopic cartilage grading. Near-infrared spectroscopy has a good interobserver correlation. Thus, this method could be developed in the future as a precise method of measuring cartilage lesions."],["dc.identifier.doi","10.1177/0363546510376744"],["dc.identifier.fs","577162"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6179"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60305"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","Near-Infrared Spectroscopy for Arthroscopic Evaluation of Cartilage Lesions: Results of a Blinded, Prospective, Interobserver Study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4641"],["dc.bibliographiccitation.issue","14"],["dc.bibliographiccitation.journal","Sensors"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Fowdur, Jaya Shradha"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Heymann, Frank"],["dc.date.accessioned","2021-09-01T06:43:02Z"],["dc.date.available","2021-09-01T06:43:02Z"],["dc.date.issued","2021"],["dc.description.abstract","As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article."],["dc.description.abstract","As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article."],["dc.identifier.doi","10.3390/s21144641"],["dc.identifier.pii","s21144641"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89206"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.publisher","MDPI"],["dc.relation.eissn","1424-8220"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Real-World Marine Radar Datasets for Evaluating Target Tracking Methods"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3410"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Sensors"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Malzer, Claudia"],["dc.contributor.author","Baum, Marcus"],["dc.date.accessioned","2021-07-05T15:00:49Z"],["dc.date.available","2021-07-05T15:00:49Z"],["dc.date.issued","2021"],["dc.description.abstract","High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments."],["dc.description.abstract","High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/s21103410"],["dc.identifier.pii","s21103410"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87911"],["dc.language.iso","en"],["dc.notes.intern","DOI Import DOI-Import GROB-441"],["dc.relation.eissn","1424-8220"],["dc.rights","CC BY 4.0"],["dc.title","Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Volkmar, Robin"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Gonzalez-Vargas, Jose"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Markovic, Marko"],["dc.date.accessioned","2020-12-10T18:39:01Z"],["dc.date.available","2020-12-10T18:39:01Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1186/s12984-019-0617-6"],["dc.identifier.eissn","1743-0003"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16682"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77511"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Improving bimanual interaction with a prosthesis using semi-autonomous control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","316"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Thormann, Kolja A."],["dc.contributor.author","Tozzi, Viola"],["dc.contributor.author","Starke, Paula"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Baum, Marcus"],["dc.contributor.author","Rosenberger, Albert"],["dc.date.accessioned","2022-08-16T12:34:13Z"],["dc.date.available","2022-08-16T12:34:13Z"],["dc.date.issued","2022-08-04"],["dc.date.updated","2022-08-07T03:11:43Z"],["dc.description.abstract","Background\r\n ImputAccur is a software tool to measure genotype-imputation accuracy. Imputation of untyped markers is a standard approach in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy for imputed genotypes is fundamental. Several accuracy measures have been proposed, but unfortunately, they are implemented on different platforms, which is impractical.\r\n \r\n \r\n Results\r\n With ImputAccur, the accuracy measures info, Iam-hiQ and r2-based indices can be derived from standard output files of imputation software. Sample/probe and marker filtering is possible. This allows e.g. accurate marker filtering ahead of data analysis.\r\n \r\n \r\n Conclusions\r\n The source code (Python version 3.9.4), a standalone executive file, and example data for ImputAccur are freely available at \r\n https://gitlab.gwdg.de/kolja.thormann1/imputationquality.git\r\n \r\n ."],["dc.identifier.citation","BMC Bioinformatics. 2022 Aug 04;23(1):316"],["dc.identifier.doi","10.1186/s12859-022-04863-z"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112733"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Imputation"],["dc.subject","Accuracy"],["dc.subject","GWAS"],["dc.subject","Marker selection"],["dc.subject","SNP"],["dc.subject","Quality control"],["dc.title","ImputAccur: fast and user-friendly calculation of genotype-imputation accuracy-measures"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]