The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Apresentamos uma abordagem alternativa ao que chamamos de “otimização padrão”, que minimiza uma função de custo através da busca em um espaço de parâmetros. Em vez disso, a nossa abordagem "projecta-se" no espaço de observação conjunta na variedade definida pela "restrição de consistência", que exige que qualquer subconjunto mínimo de observações produza o mesmo resultado. Esta abordagem evita muitas dificuldades encontradas na otimização padrão. Como exemplos típicos, aplicamos isso ao ajuste de linha e à triangulação multivista. Este último produz um novo algoritmo muito mais eficiente que os métodos existentes. Também discutimos a otimização de nossa abordagem.
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Kenichi KANATANI, Yasuyuki SUGAYA, Hirotaka NIITSUMA, "Optimization without Minimization Search: Constraint Satisfaction by Orthogonal Projection with Applications to Multiview Triangulation" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2836-2845, October 2010, doi: 10.1587/transinf.E93.D.2836.
Abstract: We present an alternative approach to what we call the "standard optimization", which minimizes a cost function by searching a parameter space. Instead, our approach "projects" in the joint observation space onto the manifold defined by the "consistency constraint", which demands that any minimal subset of observations produce the same result. This approach avoids many difficulties encountered in the standard optimization. As typical examples, we apply it to line fitting and multiview triangulation. The latter produces a new algorithm far more efficient than existing methods. We also discuss the optimality of our approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2836/_p
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@ARTICLE{e93-d_10_2836,
author={Kenichi KANATANI, Yasuyuki SUGAYA, Hirotaka NIITSUMA, },
journal={IEICE TRANSACTIONS on Information},
title={Optimization without Minimization Search: Constraint Satisfaction by Orthogonal Projection with Applications to Multiview Triangulation},
year={2010},
volume={E93-D},
number={10},
pages={2836-2845},
abstract={We present an alternative approach to what we call the "standard optimization", which minimizes a cost function by searching a parameter space. Instead, our approach "projects" in the joint observation space onto the manifold defined by the "consistency constraint", which demands that any minimal subset of observations produce the same result. This approach avoids many difficulties encountered in the standard optimization. As typical examples, we apply it to line fitting and multiview triangulation. The latter produces a new algorithm far more efficient than existing methods. We also discuss the optimality of our approach.},
keywords={},
doi={10.1587/transinf.E93.D.2836},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Optimization without Minimization Search: Constraint Satisfaction by Orthogonal Projection with Applications to Multiview Triangulation
T2 - IEICE TRANSACTIONS on Information
SP - 2836
EP - 2845
AU - Kenichi KANATANI
AU - Yasuyuki SUGAYA
AU - Hirotaka NIITSUMA
PY - 2010
DO - 10.1587/transinf.E93.D.2836
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - We present an alternative approach to what we call the "standard optimization", which minimizes a cost function by searching a parameter space. Instead, our approach "projects" in the joint observation space onto the manifold defined by the "consistency constraint", which demands that any minimal subset of observations produce the same result. This approach avoids many difficulties encountered in the standard optimization. As typical examples, we apply it to line fitting and multiview triangulation. The latter produces a new algorithm far more efficient than existing methods. We also discuss the optimality of our approach.
ER -