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The collection of the rich flow of information provided by the current generation of fast vision sensing systems brings new challenges in the selection of only relevant features out of the avalanche of data generated by those sensors. This paper discusses some aspects of intelligent sensing for advanced robotic applications, with the main objective of designing innovative approaches for automatic selection of regions of observation for fixed and mobile sensors to collect only relevant measurements without human guidance. The proposed neural-gas-network solution selects regions of interest for further sampling from a cloud of sparsely collected 3-D measurements. The technique automatically determines bounded areas where sensing is required at a higher resolution to accurately map 3-D surfaces. Therefore, it provides significant benefits over brute-force strategies as scanning time is reduced and the size of the data set is kept manageable. Experimental evaluation of this technology is presented for 3-D surface measurement and modeling.