Welcome to the Computer Vision and Machine Learning Systems Group

We are interested in interdisciplinary research questions involving the development of novel computer vision, machine learning and imaging technologies yielding new approaches to acquire and analyse data with applications in all kinds of disciplines including ecology, medicine, biology, additive manufacturing, robotics and digital humanities. This requires a fundamental investigation of how computers perceive and understand complex real-world situations. By examining the entire process from data acquisition (i.e. sensing hardware) over data interaction (e.g. augmented and virtual reality) to quantitative evaluations (i.e. algorithms) we are seeking for solutions beyond classical image analysis, pattern recognition and artificial intelligence methodologies. Many lessons remain to be learned to tackle the challenges of real-world data which we seek to study and reveal in the coming years to develop novel and sustainable data-driven systems. If you would like to get in touch with us please contact Prof. Dr. Benjamin Risse.

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Project funded by the HFSP!
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Our collaborative research project on developing novel AI algorithms to study plant pollinator interactions has been funded by the Human Frontier Science Program (HFSP). In particular we are interested in gaining insights into more sustainable and efficient agricultural food production methods. Especially in times of global warming and insect defaunation, these interactions will play an important role in the future. More information can be found here

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Paper Accepted!
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Our paper "Touch Recognition on Complex 3D Printed Surfaces using Filter Response Analysis" has been accepted at the IEEE VR 2021 workshop on on Novel Input Devices and Interaction Techniques (NIDIT). In this work demonstrate the usage sensing touch on arbitrary 3D printed geometries using filter responses in combination with deep neural networks. 

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CRC inSight is funded by the DFG!
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This research center  focuses on the question of how the body regulates inflammation in different organs and, to this end, develop a specific imaging methodology. In our project we will explore multi-scale and multimodal data integration into generative deep neural networks to identify new and explainable machine learning algorithms. For more information please visit the SFB website and for more details about our project can be found here

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CRC Intelligent Matter is funded by the DFG!
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 The CRC „Intelligent matter: From responsive to adaptive nanosystems“ is inspired by the question whether synthetic matter can provide artificial building blocks to enable intelligent material properties. In our project we will investigate nanophotonic building blocks and their properties for neural networks. More information can be found here.

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Deep Farm Bots innovation network started!
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The recently approved ZIM cooperation network Deep Farm Bots has been started. The central goal of the network is to develop and disseminate new agricultural robotics solutions for efficient and sustainable agriculture. In an interdisciplinary approach, agricultural robotics is to be linked with new methods of Deep Learning and the synergy effects between the partners are to be deepened. The network is funded by the Central Innovation Programme of the Federal Ministry for Economic Affairs and Energy. More information can be found here.

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Paper accepted at the ICPR 2020 Workshop on Explainable AI
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Our paper "The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks" has been accepted at the International Conference on Pattern Recognition (ICPR 2020) workshop on Explainable Deep Learning - AI. In this work we introduce a novel measure for neural activation sparsity along with several visualisation techniques for this property. Using these new insights we demonstrate how sparsity is correlated with overfitting, which intrinsically limits the learning capabilities of the models. The paper can be found here

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Paper accepted at the VAIB2020 ICPR workshop
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Our paper "Towards Visual Insect Camera Traps" has been accepted at the VAIB 2020 workshop which is part of the International Conference on Pattern Recognition (ICPR 2020). In this work we evaluate different deep learning strategies for small object detection in cluttered scenes such as insects in natural environments. The underlying insect in the wild dataset was generated in a collaborative effort with the Institute for Landscape Ecology (University Münster) and the Centre for Biodiversity Monitoring at the Zoological Research Museum Alexander Koenig in Bonn (Germany). Additional information can be found here: paper & video

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Paper accepted at the WACV 2021
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Our paper "Embedded Dense Camera Trajectories in Multi-Video Image Mosaics by Geodesic Interpolation-based Reintegration" has been accepted at the WACV 2021 conference. In this work we are presenting a novel algorithm to generate global large-scale image registrations with embedded dense camera trajectories. Using these algorithm we demonstrate that more than 100.000 images can be registered in reasonable time and the resultant reconstructions are widely applicable to land surveys, tracking approaches or large-scale area reconstructions. For more information you can find the publication here

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News article about our research is featured in DIE WIRTSCHAFT MS
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Our medical training research project is featured in DIE WIRTSCHAFT Münster & Münsterland. In this project we are investigating the usage of virtual and augmented reality (VR & AR) in combination with advanced user interaction metaphors and machine learning to derive novel interactive teaching strategies. If you are interested in our working group for VR and AR in Biomedical Computer Vision see here and information about the project can also be found here. The article can be found online here.

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Teaching Award of the Faculty of Mathematics & Computer Science
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We recently won the Teaching Award of the Department of Mathematics and Computer Science at the Universtiy of Münster. Many thanks for this great award. For more information please see here.

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NEW PHD STUDENT: JULIAN BIGGE
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Julian Bigge joins the group as a PhD student. He will investigate new vision and tracking techniques for model organisms. Welcome Julian!

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AI STARTER GRANT FUNDED!
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Our AI Starter Grant "Learning from Neuroscience to Investigate the "IQ" of Deep Neural Networks" has been accepted! In this project we will investigate strategies from neuroscience to explain deep neural networks. For more details see here.

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NEW PREPRINT AVAILABLE!
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Our pre-print "Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization" is now available on arXiv. In this work we investigate novel explainable AI (XAI) methodologies for deep convolutional neural networks (CNNs). The pre-print can be found here.

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NEW PHD STUDENT: DOMINIK BERSE
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Dominik Berse joins the group as a PhD student. His research will be within 3D printing project SmartPrint. Welcome Dominik!

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AI@WWU WORKSHOP
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Our AI@WWU workshop will start this month. In this course we will teach AI and machine learning basics and apply these techniques to interdisciplinary research problems. More details can be found here.

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NEW PHD STUDENT: PASCAL KOCKWELP
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Pascal Kockwelp joins the group as a PhD student. He will investigate the use auf augmented and virtual reality systems for medical education. Welcome Pascal!

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PILOT PROJECT FUNDED
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 PhD students received a funding for their interdisciplinary pilot project. In this project  four scientists from computer science, biology and medicine are developing an imaging system to assess sperm motility - an important factor in determining male fertility. Congratulations to Matthias Kiel, Sebastian Thiele, Samuel Young and Lars Haalck. For more details see here.

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PAPER ACCEPTED IN DEVELOPMENT
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Our paper "The Drosophila NCAM homolog Fas2 signals independently of adhesion" has been accepted in Development. You can find the article here.

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DHD2020 TALK
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Our abstract has been selected to be presented at the Conference DhD 2020 "Spielräume". The talk is entitled: "How deep learning-based images analysis and cultural historical heraldry benefit from each other". Thanks to everybody involved!

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NEW PHD STUDENT: EIKE GEBAUER
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Eike Gebauer joins the group as a PhD student. His research will be within 3D printing project SmartPrint. Welcome Eike!

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PAPER ACCEPTED IN JOURNAL OF NEUROSCIENCE METHODS!
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Our paper "Towards image-based animal tracking in natural environments using a freely moving camera" has been accepted in Journal of Neuroscience Methods. You can find the article here.

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VIRTUAL & AUGMENTED REALITY FOR BIOMEDICAL COMPUTER VISION
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Working group Virtual and Augmented Reality for Biomedical Computer Vision: We aim to study and implement innovative VR and AR strategies for novel biomedical computer vision applications. For more details see here.

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KMU INNOVATIV VERBUNDPROJEKT FUNDED!
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Our KMU Inno grant "Developing Intelligent 3D Printers (SMARTPRINT)" has been accepted! In this project we will develop novel computer vision and machine learning strategies to improve FDM 3D printing.

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EU & MWIDE TRANSFER PROJECT FUNDED
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Our Start-Up transfer.NRW grant was funded by the European Regional Development Fund and the Ministery of Economy, Innovation, Digitisation and Energy (NRW). This academic transfer funding will support the development of insect tracking systems within the academic spin-out Qubeto GmbH. Congratulations!

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ACCEPTED ENVIROINFO 2019 PRESENTATION
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Our manuscript and talk "Quantifying the Behavioural Dynamics Behind the Sixth Mass Extinction of Insects" has been accepted at this years EnviroInfo 2019 conference in Kassel. Lars Haalck will present his progress on our insect tracking system. Congratulations to everybody involved! More information can be found here.

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PAPER ACCEPTED IN IEEE/ACM TRANS COMP BIO!
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Our paper "Resolving colliding larvae by fitting ASM to random walker-based pre-segmentations" has been accepted in IEEE/ACM transactions on computational biology and bioinformatics. You can find the article here.

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PAPER ACCEPTED IN PLOS COMP BIO!
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Our paper "From skylight input to behavioural output: a computational model of the insect polarised light compass" has been accepted in the PLoS computational biology. You can find the article here.

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MICROSOFT AI FOR EARTH GRANT ACCEPTED!
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We are very pleased that our insect tracking project is now funded by the Microsoft AI for Earth initiative. In this project we are investigating the limitations of current visual tracking algorithms for very small objects in cluttered environments and aim to develop novel strategies for ecological research. 

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TALK AT THE BBC IN MANCHESTER
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We are invited to present our work in in-field animal tracking at the BBC in Manchester in April 2019. In particular we will speak about "Detecting and Tracking Animals in Complex Natural Environments". 

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NEW BIORXIV PAPER!
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Our paper "A Multi-Purpose Worm Tracker Based on FIM" is now available on bioRxiv. The article can be found here.

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PAPER ACCEPTED AT GCPR 2018!
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Our paper "Deep Distance Transform to Segment Visually Indistinguishable Merged Objects" has been accepted at GCPR 2018.

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PAPER ACCEPTED IN NATURE COMMUNICATIONS!
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Our paper "The sulfite oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia" has been accepted in the Nature Communications. You can find the article here.

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PAPER ACCEPTED AT MEASURING BEHAVIOR 2018!
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Our paper "Possibilities, Constraints and Limitations of Image-based Animal Tracking in Natural Environments" from Benjamin Risse, Michael Mangan and Barbara Webb has been accepted at the Measuring Behavior 2018 (MB2018) conference.

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PAPER ACCEPTED IN COMPUT. BIOL. MED.!
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Our paper "Automatic non-invasive heartbeat quantification of Drosophila pupae" from Dimitri Berh, Aaron Scherzinger, Nils Otto, Xiaoyi Jiang, Christian Klämbt and Benjamin Risse has been accepted in the Computers in Biology and Medicine.

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NEW ELECTRONIC WORKSPACE!
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Our new electronic workspace is now in place. Students interested in micro-computers, low-level programming or working on little hardware projects please contact b.risse@uni-muenster.de.