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.
June 2019 - The MaSTr1325 dataset for training deep USV obstacle detection models accepted to IROS2019.
May 2019 - Segmentation-Based Deep-Learning Approach for Surface-Defect Detection published in Journal of Intelligent Manufacturing, 2019. Now available code and KolektorSDD dataset.
May 2019 - Deep Learning for Large-Scale Traffic-Sign Detection and Recognition published in IEEE Transactions on Intelligent Transportation Systems, 2019. Now available code and DFG-TSD dataset.
February 2019 - Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters accepted to CVPR 2019.
February 2019 - Obstacle Tracking for Unmanned Surface Vessels using 3D Point Cloud accepted IEEE Journal of Oceanic Engineering.
December 2018 - FuCoLoT - A Fully-Correlational Long-Term Tracker accepted to Asian Conference on Computer Vision (ACCV) 2018 as an oral presentation.
September 2018 - Towards automated scyphistoma census in underwater imagery: a useful research and monitoring tool accepted to Journal of Sea Research.
July 2018 - TensorFlow implementation of DAU ConvNet from Spatially-Adaptive Filter Units for Deep Neural Networks paper now available.
March 2018 - Spatially-Adaptive Filter Units for Deep Neural Networks accepted to Computer vision and pattern recognition, CVPR2018. Now available code and pre-trained models .
March 2018 - Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation accepted to Robotics and Autonomous Systems.