Research Goals

In the CVMLS group we have specialized on data science methodologies including artificial intelligence, deep learning, computer vision and image processing and we are particularly interested in investigating data science technologies for ecological, geological, biological and medical applications such as biodiversity monitoring, environmental modelling, animal welfare and other research areas. Besides researching into novel algorithms we are intrigued by including new and/or customised hardware components like state-of-the-art sensors and actuators. This multi-level combination is particularly promising to bridge the gap between different scientific disciplines and in order to identify novel solutions for technically challenging and socially important questions. Our ultimate research goal is to enhance our understanding of potentially deep machine learning and computer vision algorithms while developing novel solutions with real-world applicability.

Current and past research projects can be found in the projects section and our research ouput is listed in the publications site. If you have any questions regarding our research please contact b.risse@uni-muenster.de.

Research Scheme

Computers are arguably the most versatile man-made technology of our time. These unversal machines are deeply integrated into our society and their transversal character touches all academic and non-academic disciplines alike. Especially the capability of computers to process and analyse visual data (computer vision) and to automatically derive pattern within data (machine learning) enable these machines to facilitate complex intellectual tasks (colloquially referred to artificial intelligence).

Despite the recent successes of computer vision (CV) and machine learning (ML), the application of these systems to real-world problems is however still challenging. In our group we explore these challenges by investigating the theoretical limitations of modern CV and ML algorithms in order to derive abstract and generalisable solutions. Ideally, these solutions feed back into interdisciplinary applications in order to improve automatised and high-throughput data analyses while enabling the transfer of these technologies into the industry and the society.

Besides the algorithmic focus of our group we are also interested in state-of-the-art hardware systems such as innovative imaging sensors and 3D printing developments. In fact, only if the entire pipeline from data acquisition over processing to the data output / actuation is considered jointly, we will be able to pave the way for comprehensive data driven and sustainable technologies. Due to this systematic approach we refer to our research as Computer Vision & Machine Learning Systems (CVMLS).

© CVMLS

Given the universal character of computers in general and CVMLS in particular our application areas include but are not limited to: ecology, medicine, biology, engineering and humanities. Moreover, more theoretical perspectives from mathematics and physics are pivotal for our work. This broad range of academic intersections is reflected in a variety of interdisciplinary projects and collaborations.

  • Research Interests

    Computer Science

    • Computer Vision (esp. 2D/3D tracking, motion compensation, object detection)
    • Machine Learning (esp. deep learning, CNNs, global optimisation)
    • Image Processing (esp. image filtering, transformations, calibration)
    • Computer Graphics (esp. scene reconstruction, virtual and augmented reality)
    • Robotics (esp. navigatoin strategies, visual route identification)

    Engineering

    • 3D Printing (esp. FDM printing, autonomous printing supervision)
    • Imaging Techniques (esp. touch techniques, IR / UV/ POL imaging)
    • Sensor Fusion (esp. multi-camera systems, fusion of genetic techniques)

    Biomedicine & Ecology

    • Behavioural Biology (esp. navigation, insect eyes, laboratory / field experiments)
    • Neuroscience (esp. analysis of Drosophila flies / larvae, cell motion analysis)
    • Artificial Life (esp. simulated evolution)
    • Conservation (esp. insect defaunation)

    Digital Humanities

    • Computer Science Ethics and Implications (esp. artificial intelligence)
    • Computer Vision and Machine Learning in Humanities