The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision
Technologies such as video surveillance and vision guided robotics require flexible vision systems that interpret the scene according to the current task at hand. Attention has been suggested to play an important role in the process of scene understanding by prioritizing relevant information. However, the underlying processes that allow cognition to guide vision have not been fully explored. Our procedure has its origin in current findings of research in attention. We suggest an approach in which high-level cognitive processes are top-down directed and modulate stimulus signals such that vision is a constructive process in time. Prior knowledge is combined with the observation taken from the image by a population-based inference in order to dynamically update the conspicuity of each feature. Any decision, such as object detection, is based on these distributed conspicuities. We demonstrate this concept on a goal-directed object detection task in natural scenes.
attention cognitive control natural scenes object detection object recognition top-down inference computational neuroscience