Prof. Heikki Kälviäinen heads the Computer Vision and Pattern Recognition Laboratory at the Department of Computer Engineering at Lappeenranta-Lahti University of Technology in Finland. His research focuses on computer and machine vision problems, pattern recognition and machine learning. Prof. Kälviäinen prepared a very interesting lecture combining a number of research areas in artificial intelligence. In the first part, mainly aimed at those interested in learning about AI principles, he will introduce basic approaches to digital image processing and machine learning analysis. The second part is devoted to selected sample applications of machine vision: automated recognition of Saimaa seal individuals, assessment of Baltic Sea plankton, and prediction of future board quality based on observations of the outer surface of logs.
Title: Computer vision using machine learning: challenges and solutions
The presentation considers computer vision, especially a point of view of machine learning 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.