IEEE Trans Image Process - On optimal dynamic sequential search for matching in real-time machine vision.

Tópicos

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Resumo

In the matching tasks of tracking and geometrical vision, there are usually priors available on the absolute and/or relative image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search that can achieve global matching with much fewer image processing operations and lower overall computational cost. First, the concept of dynamic sequential search (DSS) is presented. Then, the problem of determining an optimal search order for DSS is investigated, when the probabilistic distribution of the features can be described by a multivariate Gaussian model. Based on the general formulas for sequentially updating the predicted positions of the features as well as their innovation covariance, the theoretic lower bound for the sum of the areas of the features search-regions is derived, and the necessary and sufficient condition for the optimal search order to approach this lower bound is presented. After that, an algorithm for dynamically determining a suboptimal search order is presented, with a computational complexity of O(n3), which is two magnitudes lower than those of the state-of-the-art algorithms. The effectiveness of the proposed method is validated by both statistical simulation and real-world experiments with a monocular visual SLAM (simultaneous localization and mapping) system. The results verify that the performance of the proposed method is better than the state-of-the-art algorithms, with both fewer image processing operations and lower overall computational cost.

Resumo Limpo

match task track geometr vision usual prior avail absolut andor relat imag locat featur interest paper use prior dynam guid featur featur match search can achiev global match much fewer imag process oper lower overal comput cost first concept dynam sequenti search dss present problem determin optim search order dss investig probabilist distribut featur can describ multivari gaussian model base general formula sequenti updat predict posit featur well innov covari theoret lower bound sum area featur searchregion deriv necessari suffici condit optim search order approach lower bound present algorithm dynam determin suboptim search order present comput complex two magnitud lower stateoftheart algorithm effect propos method valid statist simul realworld experi monocular visual slam simultan local map system result verifi perform propos method better stateoftheart algorithm fewer imag process oper lower overal comput cost

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