Abstract
The performance of many autonomous systems based on artificial vision depends mainly on the speed of response and the field of view of the vision systems. The many tasks to be carried out, such as object detection, recognition, tracking, etc., the complexity of reliable algorithms and tasks to be done in real time, and the huge data volumes involved with stereo vision systems, imply processing times and resources that, in some cases, are incompatible with or unsuitable for acceptable system operation. Multiresolution systems are one alternative to cover wide fields of view without involving high data volumes and, therefore, considerably reduce the constraints imposed by off-the-shelf uniresolution vision systems.
Our work is related to adaptive space-variant sensors, able to supply any number of resolution levels with reconfigurable resolution profiles around regions or objects of interest, and to the specific algorithms and hierarchical data structures related to processing multiresolution data involved in tasks of image segmentation, object detection, etc. required for operation in dynamic environments.
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Camacho, P., Arrebola, F., Sandoval, F. (2003). Multiresolution Vision in Autonomous Systems. In: Zhou, C., Maravall, D., Ruan, D. (eds) Autonomous Robotic Systems. Studies in Fuzziness and Soft Computing, vol 116. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1767-6_17
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DOI: https://doi.org/10.1007/978-3-7908-1767-6_17
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