Vision is a very natural capability and it is commonly accepted that about 80% of what we perceive is vision-based. Vision’s highly intuitive nature makes it difficult for us to understand the myriad of problems associated with designing AVS, in contrast to sophisticated analytic tasks such as playing chess.
Thus AVS became a widely underestimated scientific problem, maybe one of the most underestimated problems of the past decades.
Many AI researchers believed that the real challenges were symbolic and analytic problems and visual perception was just a simple sub-problem, to be dealt with in a summer project, which obviously failed.
The truth is that computers are better than humans at playing chess, but even a small child has better generic vision capabilities than any artificial system.
My research aims at improving AVS capabilities substantially, driven by an HVS-inspired approach, as AVS are supposed to coexist with – and therefore predict actions of – humans.
Many AI researchers believed that the real challenges were symbolic and analytic problems and visual perception was just a simple sub-problem, to be dealt with in a summer project, which obviously failed.
The truth is that computers are better than humans at playing chess, but even a small child has better generic vision capabilities than any artificial system.
My research aims at improving AVS capabilities substantially, driven by an HVS-inspired approach, as AVS are supposed to coexist with – and therefore predict actions of – humans.
