Abstract
The human visual cortex performs salient region detection, a process critical to the rapid understanding of a scene. This is performed on large arrays of locally interacting neurons that are slow to simulate sequentially. In this paper we describe and evaluate a novel, bio-inspired, cellular automata (CA) architecture for the determination of the salient regions within a scene. This parallel processing architecture is appropriate for implementation on a graphics processing unit (GPU). We compare the performance of this algorithm against that of CPU implemented salient region detectors. The CA algorithm is less subject to variation due to changing scale, viewpoint and illumination conditions. Also due to its GPU implementation, this algorithm is able to detect salient regions faster than the CPU implemented algorithms.
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Jones, D.H., Powell, A., Bouganis, CS., Cheung, P.Y.K. (2010). A Salient Region Detector for GPU Using a Cellular Automata Architecture. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_62
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DOI: https://doi.org/10.1007/978-3-642-17534-3_62
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