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Professorship of Applied Sciences Computer Vision & Data Science
The Dutch AI coalition writes that artificial intelligence is crucial for the maintenance and enhancement of our international competitive positions.
The processing of image data (photos and videos) is one of the biggest challenges, as it is estimated that 80% of all data in the world is processed in this way. As a nationally recognised expert centre in the field of pattern recognition and image processing, the Computer Vision & Data Science professorship has been contributing to the anchoring of artificial intelligence in the Netherlands for over 25 years.
Computer Vision & Data Science is about solving automation problems in the field of visual inspections. Problems in the field of image acquisition, image processing and recognition of patterns in that image information with techniques from artificial intelligence such as deep learning and machine learning. These include quality control, automatic position and orientation determination, disease detection, defect measurement and product sorting.
The strength of the professorship is that it has both knowledge and equipment for the entire chain of illumination, cameras, optics, set-up, vision algorithms, deep learning algorithms and their implementation in existing software systems.
The four focus areas of the Computer Vision & Data Science professorship's applied research are: high-performance image acquisition, applied computer vision, state-of-the-art data science and education. The application is broad, from precision agriculture to medical. The goal and the approach are the same: to work from everyone's own strengths, in cooperation with students, staff and business experts, on state-of-the-art practice-oriented research projects in order to develop new knowledge in the field of computer vision and data science.
The Computer Vision & Data Science professorship is therefore at the cutting edge of science and practice. Our challenge is to bring (the latest) scientific knowledge in the field of image data analysis into practice. Current research themes we are working on, the translation of which to practical application still has many challenges and possibilities, are: hyperspectral imaging, anomaly detection, object tracking and explainable AI. Since the professorship works on the development of algorithms, artificial intelligence, to make the application suitable and affordable for businesses and organisations, the nature of this research is innovative and can often be done in a subsidised project.
Application examples
A lot of GPU power is needed for all the experiments that the students and the team carry out during their research. Therefore, we keep updating our facilities with powerful hardware. In addition to our supercomputer called Deep Frisian, we have expanded our server network with even more computing power, bringing our capacity to 576GB of GPU memory. We combine our super computers with deep learning software algorithms that can identify visual patterns in all types of data. This is a powerful combination and the possibilities are endless.
The professorship works on automating visual inspections, such as inspecting fields with smart civilian drones and inspecting of ships on corrosion. For its research the professorship has a wide range of state-of-the-art cameras at its disposal. For example, hyper-spectral cameras to take accurate measurements in light that is not visible to humans.
Seam leakage' after an operation is a worrying problem. In the project LapVas, the Computer Vision & Data Science professorship searched for a measuring instrument that maps the quality of the intestinal wall tissue. With the aid of the measuring instrument, the micro(blood) circulation of the tissue is made visible. This enables the operating surgeon to make a better decision. The aim is that fewer intensive care admissions will be necessary, the patient will recover more quickly and fewer recovery operations will be needed. Various tests have been carried out with the measuring instrument.
Besides the MCL in Leeuwarden, the Biomedical Photonic Imaging department of the University of Twente is also actively involved in the LapVas study. The project is part of the broader research project OK of the Future by innovation agency LIMIS, which investigates innovations in healthcare.
The recycling of plastics is a well-known social challenge. The Computer Vision & Data Science professorship works together with the Circular Plastics professorship on this issue. If plastic can be sorted accurately enough, the recycling chain will work more effectively and it will no longer be necessary to burn plastic. The central question is: How can this sorting be improved using techniques from computer vision and data science?
In this project, a hyper-spectral camera is used that is sensitive to Short Wave Infrared (SWIR). In this part of the electromagnetic spectrum, it is possible to distinguish various polymers on the basis of chain length (and therefore type, PE, PP, PVC, etc.).
By using the latest techniques from artificial intelligence (e.g. deep learning), recycling systems can be trained in a data-driven way. With this approach, not only the spectral properties of polymers are included in the sorting, but also the morphological properties. In the first phase of the research, promising results were obtained that demonstrate the added value of using deep learning in the context of polymer recycling.
Collaboration partners
The Computer Vision & Data Science professorship is the link between higher education and the professional field. We help companies to innovate by making academic knowledge applicable. We share this knowledge with students so that they can strengthen the business world in the future as innovative professionals.
To achieve this, the professorship is active as a researcher and educator, but also as an entrepreneur. The professorship has been working intensively with other parties for years. As a result, we have built up an extensive network of relationships with companies, suppliers, universities and knowledge institutes with whom we share knowledge and exchange experiences. The Cluster Computer Vision North Netherlands is a platform, established by the research group, of more than thirty companies in the north of the Netherlands that are engaged in computer vision (www.ccvnn.nl). The Plaform Image Processing for the hbo was also founded, in which the NHL Stenden plays a pioneering role.
Over the years, the Computer Vision & Data Science professorship has built up a large national and international network. In addition to students from NHL Stenden's own programmes such as Computer Science, Mechanical Engineering, Chemical Technology, Technical Informatics, Mathematical Engineering and Electrical Engineering, there are also contacts with colleges abroad. Students from educational institutions in Spain, China and Mexico regularly come to Leeuwarden to work on research projects of this research group.
The Computer Vision & Data Science professorship falls under the Research Unit of the Technology & Innovation Academy, together with the professorships Sustainable Plastics, Circular Plastics and Water Technology. In addition, the professorship is linked to the Smart Sustainable Industries focus area of NHL Stenden University.
Education
With its own minor and master in Computer Vision & Data Science, the professorship offers a continuous learning line for specialisation in image data analysis. The student specialises in acquiring and processing raw sensor data (images) into information (measurements in images) and ultimately into knowledge, using Artificial Intelligence methods. We focus on image acquisition, image processing and recognising patterns in that image information with, among other things, deep learning and machine learning.
Students learn the subject by working on current practice-oriented research projects from the field, under the supervision of a technical expert from the Computer Vision & Data Science professorship. Through the professorship, students also have access to state-of-the-art equipment such as high performance computers with GPUs, 3D cameras, high speed cameras, hyper- and multispectral cameras, surround cameras, industrial cameras, but also a wide range of optics and lighting.
Team
Within the Computer Vision & Data Science professorship, various disciplines work together on the development of technological innovations. Professor Jaap van de Loosdrecht is the key figure in the team. Besides the researchers, students also play an active role within the team.
Team members
- Klaas Dijkstra
- Robin Mills
- Maya Aghaei
- Martin Dijkstra
- Willem Dijkstra
- Henry Maathuis
- Lucas Ramos
- Meintsje de Vries
Contact
NHL Stenden University of Applied Sciences
Academy Technology & Innovation
Professorship Computer Vision & Data Science
E-mail: cvds@nhlstenden.com
LinkedIn: https://www.linkedin.com/company/computer-vision-data-science