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Abstract

The question of how humans learn to categorize objects and events has been at the heart of cognitive and neuroscience research for the last decades. In recent years, much work also in computer vision has focused on this topic and by now has generated multiple challenges, databases, and novel approaches. In this talk, I will argue that there is more to "vision" than "bags of words". Recent work in our lab has focused on using state-of-the-art computer graphics and simulation technology in order to advance our understanding of the role vision plays in the "ultimate cognitive system" - the human. In particular, in my talk I will discuss the need for spatio-temporal object representations, as well as why we need a notion of shape and material properties in object interpretation that goes far beyond most current computer vision approaches. Most importantly, however, I will focus on multi-modal/multi-sensory aspects of object processing as one of the key elements of learning about the world through interaction. Evi
dence from several studies of haptic object processing, for example, has shown that the sense of touch is sometimes surprisingly acute in representing complex shape spaces. I will finish by showing how some of these perceptual and cognitive results can be integrated into novel, more efficient and effective vision systems.