Technical Program available
Scope
Recently, key advances in our psychological, physiological and computational
understanding of the primate visual attention system have fostered innovative
computational architectures for visual scene understanding. Especially in emerging
technological domains that include video surveillance, miniaturised mobile sensors,
and ambient intelligence systems, attentive processing has proven an efficient
strategy for the real-time analysis of enormous amounts of data. Attentive processing
allows natural and artificial systems to cope with information overload, by
focusing higher-level analysis resources onto a rapidly and coarsely identified
subset of sensory inputs that are most relevant. Attentional selection is intimately
dependent upon being able to use knowledge about where, when and towards what
resources should be directed, orchestrating the synergy between perception,
cognition, and action towards achieving behavioral goals.
This workshop will provide an interdisciplinary forum to present and communicate
methodologies and concepts from computer vision, cognitive psychology, robotics,
autonomous systems and neuroscience with respect to theory and applications
of visual attention. We expect investigations to focus on computational models
and other artificial embodiments of attention, to outline relevant objectives
for performance comparison, to document and to investigate promising application
domains, and to discuss the new work in relation to other aspects of cognitive
vision. Contributions wich include an experimental component, for example testing
with human or animal subjects, are encouraged --- however, advancing computational
understanding of visual attention, for machine or human perception, should be
the central theme of successful submissions.
Related literature:
Neurobiology
of Attention, eds., Itti, L., Tsotsos, J.K., Rees, G.
Attention
and Performance in Computational Vision, eds., Paletta, L., Tsotsos, J.K., Rome,
E., Humphreys, G.W.
Topics of interest include, but are not limited to, the following:
- Computational architectures for attention
- Attention and control of vision processes
- Attention in object recognition and detection
- Selection and integration of information
- Machine learning and feature selection in computer vision
- Decision making and computer vision
- Behavior engineering
- Robust statistical techniques
- Perceptual organisation
- Artificial intelligence and knowledge representation for attention
- Neural network modeling for attention
Applications:
- Video analysis and multimedia
- Surveillance
- Industrial inspection
- Robotics
- Nomadic computing
- Medical computer vision
- Mobile Mapping
- Remote sensing
- Video compression
- Computer graphics
- Data mining