This paper describes a novel algorithm especially devoted to detect "interesting" objects defined as a set of close measured values that spring out from a uniform or textured background. In particular such an algorithm has been developed with the purpose to define a movement strategy for a remotely piloted rover useful in applied geophysics. The method could be represented by a Viper that moves with a random trial over the image with the purpose to detect the presence of an object emerging from the background, and to adapt the sampling interval to the shape of the detected object. Simulations on real geophysics images show good results with reduced algorithm complexity.
Object Detection Using a Novel 2D-Adaptive Sampling Strategy / Avagnina, D.; LO PRESTI, Letizia; Mulassano, P.. - STAMPA. - (2000), pp. 627-650. (Intervento presentato al convegno Melecon 2000 Mediterranean Electrotechnical conference tenutosi a Cyprus nel 29 - 31 maggio) [10.1109/MELCON.2000.880012].
Object Detection Using a Novel 2D-Adaptive Sampling Strategy
LO PRESTI, Letizia;
2000
Abstract
This paper describes a novel algorithm especially devoted to detect "interesting" objects defined as a set of close measured values that spring out from a uniform or textured background. In particular such an algorithm has been developed with the purpose to define a movement strategy for a remotely piloted rover useful in applied geophysics. The method could be represented by a Viper that moves with a random trial over the image with the purpose to detect the presence of an object emerging from the background, and to adapt the sampling interval to the shape of the detected object. Simulations on real geophysics images show good results with reduced algorithm complexity.Pubblicazioni consigliate
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https://hdl.handle.net/11583/1413523
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