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Learning the Shape of Image Moments for Optimal 3D Structure Estimation

Paolo Robuffo Giordano 1 Riccardo Spica 1 François Chaumette 1
1 Lagadic - Visual servoing in robotics, computer vision, and augmented reality
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : — The selection of a suitable set of visual features for an optimal performance of closed-loop visual control or Structure from Motion (SfM) schemes is still an open problem in the visual servoing community. For instance, when considering integral region-based features such as image moments, only heuristic, partial, or local results are currently available for guiding the selection of an appropriate moment set. The goal of this paper is to propose a novel learning strategy able to automatically optimize online the shape of a given class of image moments as a function of the observed scene for improving the SfM performance in estimating the scene structure. As case study, the problem of recovering the (unknown) 3D parameters of a planar scene from measured moments and known camera motion is considered. The reported simulation results fully confirm the soundness of the approach and its superior performance over more consolidated solutions in increasing the information gain during the estimation task.
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https://hal.inria.fr/hal-01121630
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Paolo Robuffo Giordano, Riccardo Spica, François Chaumette. Learning the Shape of Image Moments for Optimal 3D Structure Estimation. IEEE Int. Conf. on Robotics and Automation, ICRA'15, May 2015, Seattle, United States. ⟨hal-01121630⟩

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