Visual Cognitive Systems Laboratory
Večna pot 113
tel.: +386 1 479 8245
The Visual Cognitive Systems Laboratory is involved in basic research in visually enabled cognitive systems, with emphasis on visual learning and recognition.
Research focuses on various theories about requirements, architectures, forms of representation, and varieties of mechanisms relevant to integration and control of vision systems.
Applications include tracking, learning, recognition and categorisation of objects and scenes in visual cognitive tasks, such as surveillance and smart vision-based positioning as well as in other applications of cognitive systems, such as mobile robots and cognitive assistants.
March 2017 - Learning part-based spatial models for laser-vision-based room categorization accepted to International Journal of Robotics Research.
March 2017 - Discriminative Correlation Filter with Channel and Spatial Reliability accepted to Computer vision and pattern recognition, CVPR2017.
October 2016 - The laboratory co-organized the Visual Object Tracking Challenge workshop (VOT2016) at ECCV2016 in Amsterdam, Netherlands.
January 2016 - Visual tracking using anchor templates accepted to IEEE Winter Conference on Applications of Computer Vision, WACV2016.
January 2016 - Visual object tracking performance measures revisited accepted to IEEE TIP.
January 2016 - Two papers on room categorization in service robots accepted to ICRA2016.
January 2016 - The VOT methodology paper accepted to IEEE TPAMI.
December 2015 - The laboratory co-organized the Visual Object Tracking Challenge workshop (VOT2015) at ICCV2015 in Santiago de Chile, Chile.
December 2015 - A student member of our laboratory, Alan Lukežič, received faculty Prešern award for his master thesis.
September 2015 - A student member of our laboratory, Domen Rački, received best paper award at ERK2015 - Pattern recognition section.
July 2015 - Adding discriminative power to a generative hierarchical compositional model using histograms of compositions published in Computer Vision and Image Understanding.
March 2015 - Fast image-based obstacle detection from unmanned surface vehicles accepted to IEEE Transactions on Cybernetics.
November 2014 - The semantic segmentation model for obstacle detection in USVs presented at ACCV2014, Matlab code available at Research/UnmannedSurfaceVehicles.
October 2014 - Student project RoBoat2014 finished and promo video available.
September 2014 - A new tree bark image data set available on the website.