Forschungsschwerpunkte
Computer Vision
Computer Vision; Image Processing; Computer Graphics
Machine Learning
Deep Learning; Pattern Recognition
Engineering
3D Printing; Robotics; Embedded Systems; Imaging Techniques; Sensor Fusion
Biomedicine
Behavioural Biology; Neurobiology; Artificial Life
Weitere Zugehörigkeiten an der Universität Münster
Vita
Akademische Ausbildung
- Professor, University of Münster, Münster, Germany
- Junior Professor (W1), University of Münster, Münster, Germany
- Ph. D. studies, University of Münster, Münster Germany
- Diplom Informatiker (Dipl. Inf.; MSc equivalent), University of Münster, Münster, Germany
Beruflicher Werdegang
- Professor, University of Münster, Münster, Germany
- Junior Professor (W1), University of Münster, Münster, Germany, Faculty of Mathematics and Computer Science
- Research Associate, University of Edinburgh, Edinburgh, United Kingdom, Institute for Action, Perception and Behaviour (Insect Robotics Group)
- Ph. D. studies, University of Münster, Münster, Germany, Supervisors: Prof. X. Jiang (Pattern Recognition and Image Analysis) & Prof. C. Klämbt (Institute for Neuro- and Behavioural Biology)
- Diploma degree in computer science with a minor in biology, University of Münster, Münster, Germany
Preise
- Paper of the Month – Medizinische Fakultät der Universität Münster
- Lehrpreis – Universität Münster
- Lehrpreis – Fachschaft für Mathematik & Informatik der Universität Münster
- BMVC Outstanding Reviewer Award – The British Machine Vision Association (BMVC)
- Preis für Praktische Informatik (1. Platz) – IHK Nord Westfalen
Mitgliedschaften und Aktivitäten in Gremien
- German Informatics Society Membership, Gesellschaft für Informatik e.V. (GI)
- IEEE Membership, Young Professionals and Computer Society Membership.
Rufe
WWU Münster, Geoinformatics (W2) – angenommen- Ruf auf Junior Profesur für praktische Informatik (W1), Universität Münster
WWU Münster, Praktische Informatik (W1) – angenommen
Lehre
Seminar
- Seminar: Introduction to Software Programming [148968]
[ - | | wöchentlich | Di | StudLab GEO1 130 | Prof. Dr. Benjamin Risse]
[ - | | wöchentlich | Do | StudLab GEO1 130 | Prof. Dr. Benjamin Risse]
Sonstige Lehrveranstaltungen
- Projektveranstaltung: Study Project: Animal Welfare - Tracking Zebrafish in Laboratory Conditions [149070]
- Project Management [148962]
(zusammen mit Christian Remfert)
[ - | Prof. Dr. Benjamin Risse]
[ - | Prof. Dr. Benjamin Risse]
[ - | Prof. Dr. Benjamin Risse]
Vorlesungen
- Vorlesung: Core Topics in Geographic Information Science [146958]
(zusammen mit Jun.-Prof. Jakub Krukar, Prof. Dr. Angela Schwering, Prof. Dr. Edzer Pebesma, Prof. Dr. Christian Kray) - Ringvorlesung: Ringvorlesung Geoinformatik [146972]
(zusammen mit Dr. Thomas Bartoschek, Prof. Dr. Angela Schwering, Prof. Dr. Edzer Pebesma, Prof. Dr. Christian Kray)
Seminar
- Seminar: Machine Learning for Visual Spatio-Temporal Data [146975]
Praktikum
- Praktikum: Geosoftware I [146957]
(zusammen mit Dominik Drees)
Sonstige Lehrveranstaltung
- Projektveranstaltung: Studienprojekt 1 - Machine Learning trifft Insect Monitoring [146980]
Seminar
- Seminar: Introduction to Software Programming [144970]
Sonstige Lehrveranstaltung
- Project Management [144961]
(zusammen mit Christian Remfert)
Vorlesung
- Ringvorlesung: Ringvorlesung Geoinformatik [142961]
(zusammen mit Dr. Thomas Bartoschek, Prof. Dr. Angela Schwering, Prof. Dr. Edzer Pebesma, Prof. Dr. Christian Kray)
Seminare
- Seminar: Geoinformatics Forum Discussion Group [142982]
- Seminar: Machine Learning for Visual Spatio-Temporal Data [142986]
Praktikum
- Praktikum: Geosoftware I [142960]
(zusammen mit Dominik Drees)
Kolloquium
- Kolloquium: Geoinformatics Forum [142967]
- Seminar: Introduction to Software Programming [148968]
Projekte
- InFlame – Else Kröner Medical Scientist Kolleg Münster - Dynamik von Entzündungsreaktionen ( – )
participations in other joint project: Else Kröner Medical Scientist Kolleg | Förderkennzeichen: 2021_EKMK.13 - ReproTrack.MS – Centre for Research and Development of Reproductive Scientists ( – )
participations in bmbf-joint project: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 01GR2303 - SPP 2363 - Teilprojekt: Neuronale Fingerabdrücke als struktur- und aktivitätssensitive molekulare Darstellungen ( – )
Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Schwerpunktprogramm | Förderkennzeichen: KO 4689/7-1; RI 2938/3-1 - InterKI – Interdisziplinäres Lehrprogramm zu maschinellem Lernen und künstlicher Intelligenz ( – )
Gefördertes Einzelprojekt: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 16DHBKI049 - Friends with benefits? A holistic approach to diffuse mutualism in plant pollinator interactions ( – )
participations in other joint project: HFSP - Research Grant - Program | Förderkennzeichen: RGP0057/2021 - RE-CARE – Resiliente Gesundheitssysteme in Zeiten Multipler Krisen: Eine Deutsch-Japanische Kooperation ( – )
Gefördertes Einzelprojekt: DFG - Internationale Kooperationsanbahnung | Förderkennzeichen: KR 5454/1-1 - maQinto – Maschinell trainierter Qualitätssensor, intelligente Prozessteuerung und ein ML-Framework zur ressourceneffizienten, maßgeschneiderten Kohlenstofffaserherstellung ( – )
participations in bmbf-joint project: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 01I522020D - SFB 1450 B04 - Multiskalige Darstellung und Analyse der Leukozytenwanderung in hypoxischen Entzündungsbereichen in vivo ( – )
Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Sonderforschungsbereich | Förderkennzeichen: SFB 1450/1, B04 - SFB 1459 C05 - Kohärente nanophotonische neuronale Netzwerke mit adaptiven molekularen Systemen ( – )
Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Sonderforschungsbereich | Förderkennzeichen: SFB 1459/1, C05 - meditrain – Verbundprojekt: Intelligente Virtuelle Agenten für die Medizinische Ausbildung (medical tr.AI.ning) - Teilvorhaben: Entwicklung und Erforschung von intelligenten virtuellen Agenten für die klinisch-medizinische Ausbildung mit Schwerpunkt in KI ( – )
participations in bmbf-joint project: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 16DHBKI077 - Learning from Neuroscience to Investigate the IQ of Deep Neural Networks ( – )
Gefördertes Einzelprojekt: MKW - Förderlinie „Künstliche Intelligenz/Maschinelles Lernen“ - KI-Starter | Förderkennzeichen: 005-2010-0055 - GIGA Gebärdensprachen - Entwicklung einer 5G enabled Gebärdensprachen-Applikation ( – )
participations in other joint project: MWIKE NRW - Förderwettbewerb 5G.NRW | Förderkennzeichen: FKZ 005-2018-0104; 005-2108-0102; PtJ-Nr. 2008gif042e; 2108gif042c - Augmented and Virtual Reality in der medizinischen Ausbildung ( – )
Gefördertes Einzelprojekt: Institut für Ausbildung und Studienangelegenheiten der Medizinischen Fakultät - SMARTPRINT – KMU-innovativ Verbundprojekt: Entwicklung eines Intelligenten 3D Druckers (SMARTPRINT); Teilprojekt: Erforschung und Entwicklung der Kl-Algorithmen ( – )
Gefördertes Einzelprojekt: Bundesministerium für Bildung und Forschung | Förderkennzeichen: 02P19K121 - QuBe – QuBe (Tools for Quantitative Behaviour) - Investitionen in Wachstum und Beschäftigung ( – )
Gefördertes Einzelprojekt: MWIKE NRW - EFRE/JTF-Programm - EFRE Start-up Transfer.NRW - START-UP-Hochschul-Ausgründungen NRW | Förderkennzeichen: EFRE-0400299 - Reproduction – from Genes to Molecules and Function ( – )
Durch die Universität Münster intern gefördertes Projekt: Universität Münster-interne Förderung - Topical Programs - Das Individuum im Fokus der Lebenswissenschaften ( – )
Durch die Universität Münster intern gefördertes Projekt: Universität Münster-interne Förderung - Topical Programs - EIMD – EMID - Electron Microscopy Imaging in the Dark - Entwicklung der Nachbelichtungsfunktionalität des EMID Verfahrens ( – )
participations in other joint project: BMWK - Zentrales Innovationsprogramm Mittelstand | Förderkennzeichen: ZF4649501TS8 - Artificial Intelligence for Additive Manufacturing ( – )
Gefördertes Einzelprojekt: tapdo technologies GmbH - Entwicklung eines Verfahrens zur automatischen Erstellung von Reitsportaufnahmen ( – )
Gefördertes Einzelprojekt: LV digital GmbH - Entwicklung eines Verfahrens zur Bildverbesserung von biomedizinischen Bilddaten wie Elektronen Miktoskopie ( – )
Gefördertes Einzelprojekt: EMSIS GmbH - EXC 1003 PP-2017-10 The hatching box: Automated monitoring of the circadian clock during metamorphosis of the fly ( – )
Teilprojekt in DFG-Verbund koordiniert an der Universität Münster: DFG - Exzellenzcluster | Förderkennzeichen: PP-2017-10
- InFlame – Else Kröner Medical Scientist Kolleg Münster - Dynamik von Entzündungsreaktionen ( – )
Publikationen
- . . ‘SAM meets Gaze: Passive Eye Tracking for Prompt-based Instance Segmentation.’ Proceedings of Machine Learning Research 2023.
- . . ‘Solving the Plane-Sphere Ambiguity in Top-Down Structure-from-Motion.’ Contributed to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii. doi: 10.1109/WACV57701.2024.00345.
- . . ‘Towards a Dynamic Vision Sensor-based Insect Camera Trap.’ Contributed to the Winter Conference on Applications of Computer Vision 2024, Waikoloa, Hawaii.
- . pyAKI - An Open Source Solution to Automated KDIGO classification https://arxiv.org. doi: 10.48550/arXiv.2401.12930.
- . . ‘Human fertilization in vivo and in vitro requires the CatSper channel to initiate sperm hyperactivation.’ Journal of Clinical Investigation 134, Nr. 1. doi: 10.1172/JCI173564.
- . . ‘A Systematic Evaluation of Machine Learning--Based Biomarkers for Major Depressive Disorder.’ JAMA Psychiatry . doi: 10.1001/jamapsychiatry.2023.5083. [accepted / in Press (not yet published)]
- . . ‘Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction.’ Scientific Reports 14, Nr. 1: 2034. doi: 10.1038/s41598-024-52268-0.
- . ‘deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks.’ Computers in Biology and Medicine 179. doi: 10.1016/j.compbiomed.2024.108845.
- . . ‘Towards Estimation of 3D Poses and Shapes of Animals from Oblique Drone Imagery.’ Contributed to the ISPRS Technical Commission II Symposium 2024, Las Vegas.
- . . ‘Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity.’ Environmental Modelling and Software 2024: 106245. doi: 10.1016/j.envsoft.2024.106245.
- . . ‘VR-based Competence Training at Scale: Teaching Clinical Skills in the Context of Virtual Brain Death Examination.’ Proceedings of the ACM on Human-Computer Interaction 8: 261. doi: 10.1145/3664635.
- . . ‘Accelerating Finite-Difference Frequency-Domain Simulations for Inverse Design Problems in Nanophotonics using Deep Learning .’ Journal of the Optical Society of America B 41, Nr. 4: 1039–1046. doi: 10.1364/JOSAB.506159.
- . . ‘Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears.’ Blood Advances 8, Nr. 1: 70–79. doi: 10.1182/bloodadvances.2023011076.
- . „Virtual Reality based teaching – a paradigm shift in education?“ contributed to the 73. Jahrestagung Deutsche Gesellschaft für Neurochirurgie, Köln, . doi: 10.3205/22DGNC538.
- . . „Integration of VR into Medical Education (Workshop).“ In Würtual Reality, herausgegeben von , 72. N/A: Selbstverlag / Eigenverlag. doi: 10.25972/OPUS-31720.
- . . ‘Trail using ants follow idiosyncratic routes in complex landscapes.’ Learning and Behavior s13420-023-00615. doi: 10.3758/s13420-023-00615-y.
- . . ‘Inverse Design of Nanophotonic Devices using Dynamic Binarization.’ Optics Express 31, Nr. 10: 15747–15756. doi: 10.1364/OE.484484.
- . . ‘Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder.’ Journal of the Optical Society of America B 40, Nr. 3: B35–B40.
- . . ‘CATER: Combined Animal Tracking & Environment Reconstruction.’ Science advances 9, Nr. 16: eadg2094.
- . . ‘An overview and a roadmap for artificial intelligence in hematology and oncology.’ Journal of Cancer Research and Clinical Oncology 15: 1–10.
- . . ‘A Universal Approach to Nanophotonic Inverse Design through Reinforcement Learning.’ In CLEO 2023, paper STh4G.3, edited by , STh4G.3. Washington, DC: Optica. doi: 10.1364/CLEO_SI.2023.STh4G.3.
- . . ‘A Novel Approach to Nanophotonic Black-Box Optimization Through Reinforcement Learning.’ In Q 30 Nano-optics, edited by , 1. Bad Honnef: Deutsche Physikalische Gesellschaft.
- . . ‘EyeGuide - From Gaze Data to Instance Segmentation.’ Contributed to the The British Machine Vision Conference (BMVC), Aberdeen.
- . . ‘Adaptive Photo-Chemical Nonlinearities for Optical Neural Networks.’ Advanced Intelligent Systems 5, Nr. 12: 2300229. doi: 10.1002/aisy.202300229 .
- . . ‘Tracking Tiny Insects in Cluttered Natural Environments using Refinable Recurrent Neural Networks.’ Contributed to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii. doi: 10.1109/WACV57701.2024.00697.
- . . ‘Event-driven adaptive optical neural network.’ Science advances 9, Nr. 42: eadi9127. doi: 10.1126/sciadv.adi9127.
- . „Activation Functions in Non-Negative Neural Networks.“ contributed to the Machine Learning and the Physical Sciences Workshop, NeurIPS, New Orleans, .
- . „Diffusion Models in Dermatological Education: Flexible High Quality Image Generation for VR-based Clinical Simulations.“ contributed to the NeurIPS'23 Workshop: Generative AI for Education (GAIED), New Orleans, Louisiana, .
- . Sustainable research software for high-quality computational research in the Earth System Sciences: Recommendations for universities, funders and the scientific community in Germany FIG GEO-LEO e-docs. doi: 10.23689/fidgeo-5805.
- . . ‘Adaptive Photochemical Nonlinearities for Optical Neural Networks.’ Advanced Intelligent Systems 5, Nr. 12. doi: 10.1002/aisy.202300229.
- . „Combinatorial Optimization via Memory Metropolis: Template Networks for Proposal Distributions in Simulated Annealing applied to Nanophotonic Inverse Design.“ contributed to the Neural Information Processing Systems (NeurIPS) Workshop on AI for Accelerated Materials Design (AI4Mat-2023), New Orleans, .
- . . ‘Immersive training of clinical decision making with AI driven virtual patients-a new VR platform called medical tr.AI.ning.’ GMS Journal for Medical Education 40, Nr. 2. doi: 10.3205/zma001600.
- . „Hirntoddiagnostik in Virtual Reality – was denken Studierende darüber?“ präsentiert auf der Jahrestagung der Gesellschaft für Medizinische Ausbildung (GMA) 2023, Osnabrück, . doi: 10.3205/23GMA227.
- . „Zusammenhang von Persönlichkeitsvariablen und Leistung in der virtuellen medizinischen Ausbildung.“ contributed to the Jahrestagung der Gesellschaft für medizinische Ausbildung (GMA) 2023, Osnabrück, . doi: 10.3205/23GMA273.
- . . ‘Volumetric imaging reveals VEGF-C-dependent formation of hepatic lymph vessels in mice.’ Frontiers in cell and developmental biology 10: 949896. doi: 10.3389/fcell.2022.949896.
- . . ‘An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling.’ Science advances 8, Nr. 1: eabg9471. doi: 10.1126/sciadv.abg9471.
- . . ‘Perspectives in machine learning for wildlife conservation.’ Nature Communications 13, Nr. 1: 792–807. doi: 10.1038/s41467-022-27980-y.
- . . ‘Towards VR Simulation-Based Training in Brain Death Determination.’ In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), edited by , 287–292. New York City: Wiley-IEEE Press.
- . . ‘Narrowing Attention in Capsule Networks.’ In 26th International Conference on Pattern Recognition, edited by , 2679–2685. New York City: Wiley-IEEE Press.
- . . ‘Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia.’ In The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), edited by , 1825–1834. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPRW56347.2022.00199.
- . „Development of a nanophotonic nonlinear unit for optical artificial neural networks.“ contributed to the DPG Springmeeting 2022, Erlangen, .
- . . ‘Inverse Design of Nanophotonic Devices based on Reinforcement Learning.’ In Q 38 Photonics II, edited by , 2. Bad Honnef: Deutsche Physikalische Gesellschaft.
- . . ‘Resolving colliding larvae by fitting ASM to random walker-based pre-segmentations.’ IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, Nr. 3: 1184–1194.
- . . ‘Touch Recognition on Complex 3D Printed Surfaces using Filter Response Analysis.’ In IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), edited by , 195–200. New York City: Wiley-IEEE Computer Society Press. doi: 10.1109/VRW52623.2021.00043.
- . . ‘The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks.’ In The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks. Basel : Springer International Publishing.
- . . ‘Embedded Dense Camera Trajectories in Multi-Video Image Mosaics by Geodesic Interpolation-based Reintegration.’ Contributed to the Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii.
- . . ‘Towards Visual Insect Camera Traps.’ Contributed to the International Conference on Pattern Recognition (ICPR) Workshop on Visual observation and analysis of Vertebrate And Insect Behavior (VAIB), Milan.
- . . ‘PHOTONAI-A Python API for rapid machine learning model development .’ PloS one 16. doi: 10.1371/journal.pone.0254062.
- . . ‘PHOTON--A Python API for Rapid Machine Learning Model Development.’ arXiv preprint arXiv:2002.05426 2020.
- . . ‘The Drosophila NCAM homolog Fas2 signals independently of adhesion.’ Development 147, Nr. 2. doi: 10.1242/dev.181479.
- . . ‘Towards image-based animal tracking in natural environments using a freely moving camera.’ Journal of Neuroscience Methods 330: 108455. doi: 10.1016/j.jneumeth.2019.108455.
- . . Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization. arXiv e-print:2002.09237: CoRR.
- . . ‘From skylight input to behavioural output: a computational model of the insect polarised light compass.’ PLoS Computational Biology 15, Nr. 7: e1007123. doi: 10.1371/journal.pcbi.1007123.
- . . ‘Automatic non-invasive heartbeat quantification of Drosophila pupae.’ Computers in Biology and Medicine 93: 189–199.
- . . ‘The Sulfite Oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia.’ Nature Communications 9, Nr. 1: 3514.
- . A Multi-Purpose Worm Tracker Based on FIM bioRxiv.
- . „Possibilities, Constraints and Limitations of Image-based Animal Tracking in Natural Environments.“ contributed to the Measuring Behavior, Manchester, UK, . [online first]
- . . ‘Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes.’ Environmental Modelling and Software 99: 88–100. doi: 10.1016/j.envsoft.2017.09.018.
- . . ‘Deep distance transform to segment visually indistinguishable merged objects.’ Contributed to the Proc. of 40th German Conference on Pattern Recognition (GCPR), Stuttgart.
- . . ‘The Ol1mpiad: Concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae.’ Journal of Experimental Biology 220: 2452–2475. doi: 10.1242/jeb.156646.
- . . ‘FIMTrack: An open source tracking and locomotion analysis software for small animals.’ PLoS Computational Biology 13, Nr. 5: e1005530. doi: 10.1371/journal.pcbi.1005530.
- . . ‘A FIM-based long-term in-vial monitoring system for Drosophila larvae.’ IEEE Transactions on Biomedical Engineering 64, Nr. 8: 1862–1874.
- . . ‘Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera.’ In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, edited by , 2840–2849. New York City: Wiley-IEEE Computer Society Press. doi: 10.1109/ICCVW.2017.335.
- . . ‘Interactions among Drosophila larvae before and during collision.’ Scientific Reports 11, Nr. 6: 31564. doi: 10.1038/srep31564.
- . „Tracking, Mapping and Reconstruction. Modelling the Visual Perception of Desert Ants.“ contributed to the Animal Movement International Symposium, Lund, Sweden, .
- . „Habitat3D: Recreating the History of Visual Experience of Individual Insects.“ contributed to the International Congress of Neuroethology, Montevideo, Uruguay, .
- . „HabiTracks: Visual Tracking of Insects in Their Natural Habitat.“ contributed to the International Congress of Neuroethology, Montevideo, Uruguay, .
- . . ‘FIM2c : A Multi-Colour, Multi-Purpose Imaging System to Manipulate and Analyse Animal Behaviour.’ IEEE Transactions on Biomedical Engineering 64: 1–1.
- . ‘Quantifying subtle locomotion phenotypes of Drosophila larvae using internal structures based on FIM images.’ Computers in Biology and Medicine 63, Nr. null: 269–276. doi: 10.1016/j.compbiomed.2014.08.026.
- . „FIM2C and the Analysis of Collision Behavior.“ contributed to the Flies, Worms and Robots: Combining Perspectives on Minibrains and Behavior Conference, Sant Feliu de Guixols, Spain, .
- . . ‘Imaging Modalities for Semi-Translucent Animals and Their Impact on Quantitative Analysis.’ In VAIB Workshop, ICPR, 1–4.
- . . ‘FIM imaging and FIMTrack: Two new tools allowing high-throughput and cost effective locomotion analysis.’ Journal of Visualized Experiments 94.
- . . ‘The Drosophila FHOD1-like formin Knittrig acts through Rok to promote stress fiber formation and directed macrophage migration during the cellular immune response.’ Development 141, Nr. 6: 1366–1380. doi: 10.1242/dev.101352.
- . . ‘Stereo and Motion Based 3D High Density Object Tracking.’ Image and Video Technology 2014: 136–148. doi: 10.1007/978-3-642-53842-1_12.
- . . ‘FIM, a novel FTIR-based imaging method for high throughput locomotion analysis.’ PloS one 8, Nr. 1: e53963. doi: 10.1371/journal.pone.0053963.
- . . ‘Comparison of two 3D tracking paradigms for freely flying insects.’ EURASIP J Image Video Process 2013, Nr. 1: 57. doi: 10.1186/1687-5281-2013-57.
- . „Tracking of Colliding Larvae.“ contributed to the Conference on Neurobiology of Drosophila, New York, USA, .
- . . ‘Biomedical Imaging: a Computer Vision Perspective.’ In Computer Analysis of Images and Patterns, edited by , 1–19. Düsseldorf: Springer VDI Verlag.
- . . ‘FIM: Frustrated Total Internal Reflection Based Imaging for Biomedical Applications.’ ERCIM News 95, Nr. Image Understanding: 11–12.
- . . ‘Drosophila pupal macrophages - A versatile tool for combined ex vivo and in vivo imaging of actin dynamics at high resolution.’ European Journal of Cell Biology 2013. doi: 10.1016/j.ejcb.2013.09.003.
- . „FIM: FTIR Based Image Acquisition and Tracking.“ contributed to the Conference on Behavioral Neurogenetics of Drosophila Larva (Maggot Meeting), Ashburn, USA, .
- . . ‘Kinesin heavy chain function in Drosophila glial cells controls neuronal activity.’ Journal of Neuroscience 32, Nr. 22: 7466–7476. doi: 10.1523/JNEUROSCI.0349-12.2012.
- . . ‘3D Trajectory Estimation of Simulated Fruit Flies.’ In Proc 27th IVCNZ, Dunedin 2012.
Prof. Dr. Benjamin Risse
