Author, Institution: Karolis Ryselis, Kaunas University of Technology
Science area, field of science: Natural Sciences, Informatics, N009
Scientific Supervisor: Prof. Dr. Tomas Blažauskas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defence Board of Informatics Science Field:
Prof. Dr. Hab. Rimantas Barauskas (Kaunas University of Technology, Natural Sciences, Informatics, N009) – chairperson
Prof. Dr. Hab. Gintautas Dzemyda (Vilnius University, Natural Sciences, Informatics, N009)
Prof. Dr. Vacius Jusas (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Prof. Dr. Tomas Krilavičius (Vytautas Magnus University, Natural Sciences, Informatics, N009)
Prof. Dr. Alfonsas Misevičius (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Dissertation defence meeting was at M7 Hall at The Campus Library of Kaunas University of Technology (Studentų 48–M7, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (K. Donelaičio g. 20, Kaunas)
Annotation: The dissertation presents three algorithms that solve the problems of the dissertation. The first algorithm, Agrast-6 neural network, automatically segments depth images and finds the human body in them. Agrast-6 is based on the ideas of the SegNet neural network but uses a lot fewer parameters. The proposed neural network can be applied in larger systems where
one of the data processing steps is extracting human silhouettes from depth images. The second algorithm also segments the human body in depth images but also uses user input,
therefore it is semi-automatic. It is based on the ideas of Euclidean clustering. Three improvements are proposed – skipping segmented nodes, skipping fully-segmented branches,
and using an auto-expanding bounding box instead of a set of spheres. These improvements greatly reduce the processing time. This algorithm was successfully applied to prepare a
dataset of 220k images in a relatively short time. Both algorithms greatly speed up data processing at a cost of segmentation accuracy. The third algorithm fuses skeletons from
different Kinect sensors into a single, more accurate super-skeleton. This algorithm increases the accuracy of Kinect’s skeletal data compared to the use of a single sensor. The skeleton
fusion algorithm was applied in human skeleton load analysis during physical activities.
June 26 d. 09:00
M7 Hall at The Campus Library of Kaunas University of Technology (Studentų 48–M7, Kaunas)
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