Heikki Kälviäinen: Computer Vision Applications

HeikkiHeikki Kälviäinen has been a Professor of Computer Science and Engineering since 1999. He is the head of the Computer Vision and Pattern Recognition Laboratory (CVPRL) at the Department of Computational Engineering of Lappeenranta-Lahti University of Technology LUT, Finland. Prof. Kälviäinen’s research interests include computer vision, machine vision, pattern recognition, machine learning, and digital image processing and analysis. Besides LUT, Prof. Kälviäinen has worked as a Visiting Professor at the Faculty of Information Technology of Brno University of Technology, Czech Republic, the Center for Machine Perception (CMP) of Czech Technical University, and the Centre for Vision, Speech, and Signal Processing (CVSSP) of University of Surrey, UK, and as a Professor of Computing at Monash University Malaysia.

Computer Vision Applications

The presentation considers computer vision, especially a point of view of applications. Digital image processing and analysis with machine learning methods enable efficient solutions for various areas of useful data-centric engineering applications. Challenges with image acquisition, data annotation with expert knowledge, and clustering and classification, including deep learning method training are discussed. Different applications are given as examples based on the fresh novel data available: planktons in the Baltic Sea, Saimaa ringed seals in Lake Saimaa, and logs in the sawmill industry. In the first application the motivation is that distributions of plankton types give much information about the condition of the sea water system, e.g., about the climate change. An imaging flow cytometer can produce a lot of plankton images which should be classified into different plankton types. Manual classification of these images is very laborious, and thus, a CNN-based method has been developed to automatically recognize the plankton types in the Baltic Sea. In the second application the Saimaa ringed seals are automatically identified individually using camera trap images for assisting this very small population to survive in nature. CNN-based re-identification methods are based on pelage patterns of the seals. The third application is related to the sawmill industry. The digitalization of the sawmill industry is important for optimizing material flows and the quality. The research is focused on seeing inside the log to be able to predict which kinds of sawn boards are produced after cutting the log.

His talk takes place on Wednesday, May 11, 2022 at 13:00 in room A112.