Abstract
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.
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Rebhan, S., Richter, A., Eggert, J. (2009). Demand-Driven Visual Information Acquisition. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_13
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DOI: https://doi.org/10.1007/978-3-642-04667-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04666-7
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