Personnel

LUT Computer Vision and Pattern Recognition Laboratory (CVPRL), previously known as Machine Vision and Pattern Recognition Laboratory (MVPR), educates computer vision and machine learning experts and develops novel smart information processing methods. The goal is to create useful and significant value-added applications, especially using digital image processing and analysis. For example, research is focused on machine vision systems for the sawmill industry, medical image analysis for the efficient healthcare of eye diseases, and plankton recognition for conservation of nature.

Our main objective is to perform high quality research on computer vision, machine learning, and pattern recognition. The laboratory also serves industry as an expert organization, performs applied research and educates experts in its research fields. Our research interests include visual inspection and computational vision, biomedical imaging, and data analytics. LUT CVPRL is associated with Academy of Finland's Center of Excellence (CoE) in Research in Inverse Modeling and Imaging. The laboratory offers the Computer Vision and Pattern Recognition major subject in Computational Engineering as follows:

Our research is funded by Academy of Finland, European Commission, Finnish Funding Agency for Technology and Innovation (Tekes/Business Finland), and private companies. We co-operate with many national and international research groups, including Centre for Vision, Speech, and Signal Processing (CVSSP), University of Surrey (UniS), UK, Center for Machine Perception (CMP), Czech Technical University (CVUT), Czech Republic, Color Research Group and Saimaa Ringed Seal Research Group, University of Eastern Finland (UEF), Department of Computer Graphics and Multimedia (DCGM), Brno University of Technology, Czech Republic, Laboratory for Image & Video Engineering (LIVE), at University of Texas at Austin, USA, Visual Cognition Research Group, University of Helsinki, Finland, School of Computer Science, University of Birmingham, UK, and Uncertainty Quantification Group, Massachusetts Institute of Technology (MIT), USA.