Fast, reliable and demand-driven acquisition of visual information is the key to represent visual scenes efficiently. To achieve this efficiency, a cognitive vision system must plan the utilization of its processing resources to acquire only information relevant for the task. Here, the incorporation of long-term knowledge plays a major role on deciding which information to gather. In this paper, we present a first approach to make use of the knowledge about the world and its structure to plan visual actions. We propose a method to schedule those visual actions to allow for a fast discrimination between objects that are relevant or irrelevant for the task. By doing so, we are able to reduce the system's computational demand. A first evaluation of our ideas is given using a proof-of-concept implementation. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Rebhan, S., Richter, A., & Eggert, J. (2009). Demand-driven visual information acquisition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5815 LNCS, pp. 124–133). https://doi.org/10.1007/978-3-642-04667-4_13
Mendeley helps you to discover research relevant for your work.