In this paper, we introduce a new interpretation of the signal subspace as the solution of an unconstrained minimization problem. We show that recursive least squares techniques can be applied to track the signal subs...
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ISBN:
(纸本)0819416207
In this paper, we introduce a new interpretation of the signal subspace as the solution of an unconstrained minimization problem. We show that recursive least squares techniques can be applied to track the signal subspace recursively by making an appropriate projection approximation of the cost function. The resulting algorithms have a computationalcomplexity of O(nr) where n is the input vector dimension and r(r<n) is the number of desired eigen components. We demonstrate that this approach can also be extended to track the rank, i.e. the number of signals, at the same order of linear (approximately n) computationalcomplexity. Simulation results show that our algorithms offer a comparable and in some cases more robust performance than the spherical tracker by DeGroat, the URV updating by Stewart, and even the exact eigenvalue decomposition.
In this paper, a new method for extracting vanishing points from real images is proposed. This method exhibits a linear computational complexity and has good precision. The linear computational complexity is due to th...
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In this paper, a new method for extracting vanishing points from real images is proposed. This method exhibits a linear computational complexity and has good precision. The linear computational complexity is due to the introduction of polar space, which permits the selection of segments converging on the same vanishing point before the computation of the vanishing point itself. Extensive experimentation on real images shows that vanishing points can be identified and located even in cluttered images, and that the proposed algorithm is suitable for recovering the heading direction of a robot moving in corridors and offices.
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