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- Aanaes, H, et al.
(författare)
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Robust factorization
- 2002
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 24:9, s. 1215-1225
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Tidskriftsartikel (refereegranskat)abstract
- Factorization algorithms for recovering structure and motion from an image stream have many advantages, but they usually require a set of well-tracked features. Such a set is in generally not available in practical applications. There is thus a need for making factorization algorithms deal effectively with errors in the tracked features. We propose a new and computationally efficient algorithm for applying an arbitrary errorfunction in the factorization scheme. This algorithm enables the use of robust statistical techniques and arbitrary noise models for the individual features. These techniques and models enable the factorization scheme to deal effectively with mismatched features, missing features, and noise on the individual features. The proposed approach further includes a new method for Euclidean reconstruction that significantly improves convergence of the factorization algorithms. The proposed algorithm has been implemented as a modification of the Christy-Horaud factorization scheme, which yields a perspective reconstruction. Based on this implementation, a considerable increase in error tolerance is demonstrated on real and synthetic data. The proposed scheme can, however, be applied to most other factorization algorithms.
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40. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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Sparse Localization of Harmonic Audio Sources
- 2016
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Ingår i: IEEE/ACM Transactions on Audio, Speech, and Language Processing. - 2329-9290. ; 24:1, s. 117-129
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a novel method for estimating the locations of near- and/or far-field harmonic audio sources impinging on an arbitrary, but calibrated, sensor array. Using a joint pitch and location estimation formed in two steps, we first estimate the fundamental frequencies and complex amplitudes under a sinusoidal model assumption, whereafter the location of each source is found by utilizing both the difference in phase and the relative attenuation of the magnitude estimates. As audio recordings often consist of multi-pitch signals exhibiting some degree of reverberation, where both the number of pitches and the source locations are unknown, we propose to use sparse heuristics to avoid the necessity of detailed a priori assumptions on the spectral and spatial model orders. The method’s performance is evaluated using both simulated and measured audio data, with the former showing that the proposed method achieves near-optimal performance, whereas the latter confirms the method’s feasibility when used with real recordings.
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