Danijel Skočaj, PhD
+386 1 4768 189
Visual Cognitive Systems Laboratory
Faculty of Computer and Information Science
University of Ljubljana
The main research interests: computer vision, cognitive vision, cognitive systems, visual learning and recognition, incremental learning, interactive learning, statistical learning, cognitive architectures
In the EU FP7 project CogX we have developed a corious robot George; a complex heterogenuous distributed system for interactive learning of visual concepts in a dialogue with a tutor. Our objective was to demonstrate that a cognitive system can efficiently acquire conceptual models in an interactive learning process that is not overly taxing with respect to tutor supervision and is performed in an intuitive, user-friendly way.
In our research work we address the problem of interactive learning of categorical knowledge. We describe, implement, and analyse several teacher and learner-driven approaches that require different levels of teacher competencies and consider different types of knowledge for selection of training samples. We also introduce a formal model for describing the learning strategies, and evaluate them using the proposed evaluation measures.
One of the main research topics of our group in the past were subspace methods. We have proposed several methods for robust estimation of subspace coefficients and for learning subspace representations. By combining the properties of the reconstructive and the discriminative subspace methods we were able to extend the standard approaches into incremental and robust ones, and to show the efficiency of the proposed methods in many computer vision tasks.
The main task of the affordance learning algorithm as defined in our framework, is to identify significant clusters in the result space and associate these clusters with data in the object property space. This allow for the affordances of novel objects to be broadly classified in terms of result space clusters, by observing their respective object property features and using them as input to a classifier trained by the affordance learning algorithm.
The research group is involved in basic research in computer vision, with emphasis on visually enabled cognitive systems involving visual learning and recognition. Topics include recognition and tracking of objects, scenes, and activities in visual cognitive tasks such as smart vision-based detection and positioning using wearable computing as well as for mobile robots and cognitive assistants.
Project duration: 2009-2014.
The high level aim of this project was to develop a unified theory of self-understanding and self-extension with a convincing instantiation and implementation of this theory in a robot. By self-understanding we mean that the robot has representations of gaps in its knowledge or uncertainty in its beliefs. By self-extension we mean the ability of the robot to extend its own abilities or knowledge by planning learning activities and carrying them out. The project involved six universities and about 30 researchers.
The main goal of the project was to advance the science of cognitive systems through a multi-disciplinary investigation of requirements, design options and trade-offs for human-like, autonomous, integrated, physical (eg., robot) systems, including requirements for architectures, for forms of representation, for perceptual mechanisms, for learning, planning, reasoning and motivation, for action and communication.
The main objective of the EU FP5 project CogVis was to provide the methods and techniques that enable construction of vision systems that can perform task oriented categorization and recognition of objects and events in the context of an embodied agent.
Full list of publications
Teaching in 2012/13
- Algorithms and data structures 1 (Algoritmi in podatkovne strukture 1(Sežana))
- Distributed intelligent software technologies (Porazdeljene inteligentne programske tehnologije)
- Computer science (Računalništvo (FPP))
All information about the courses is provided on the internal pages of FRI and PeF.
Robust estimation of subspace coefficients. This package contains Matlab functions, which perform robust estimation of subspace coefficients in PCA, CCA and LDA methods.
- School of Computer Science, University of Birmingham, UK
- DFKI Saarbrucken, Germany
- ACIN, Technische Universität Wien, Austria
- PRIP Laboratory, Institute of Automation, Technical University Vienna, Austria
- Institute for Computer Graphics and Vision, Technical University Graz, Austria
- Center for Machine Perception, Czech Technical University, Prague, Czech Republic
- Computational Vision and Active Perception, Numerical Analysis and Computer Science, Royal Institute of Technology, Stockholm, Sweden
- RSS 2008 Workshop on Interactive robot learning, Zurich, Switzerland, 28 June 2008.
- ECCV 2006, Graz, Austria, 7-13 May 2006
- Cognitive Systems Kickoff, Bled, Slovenia, 28-30 October 2004
- Computer Vision Winter Workshop 2004, Piran, Slovenia, 2-4 February 2004