Visual Attention and Distributed Processing of Visual Information for the Control of Humanoid Robots
Aleš Ude, Jan Morén, Gordon Cheng · 2007
The goals of our work were twofold. On the one hand, we studied how to introduce topdown effects into a bottom-up visual attention system. We have extended the classic system proposed by (Itti et al., 1998) with top-down inhibitory signals to drive attention towards the areas with the expected features while still considering other salient areas in the scene in a bottom-up manner. Our experimental results show that the system can select areas of interest using various features and that the selected areas are quite plausible and most of the time contain potential objects of interest. On the other hand, we studied distributed computer architectures, which are necessary to achieve real-time operation of complex processes such as visual attention. Although some of the previous works mention that parallel implementations would be useful and indeed parallel processing was used in at least one of them (Breazeal and Scasselatti, 1999), this is the first study that focuses on issues arising from such a distributed implementation. We developed a computer architecture that allows for proper distribution of visual processes involved in visual attention. We studied various synchronization schemes that enable the integration of different processes in order to compute the final result. The designed architecture can easily scale to accommodate more complex visual processes and we view it as a step towards a more brain-like processing of visual information on humanoid robots. Our future work will center on the use of visual attention to guide higher-level cognitive tasks. While the possibilities here are practically limitless, we intend to study especially how to guide the focus of attention when learning about various object affordances, such as for example the relationships between the objects and actions that can be applied to objects in different situations.