High Accuracy Homography Computation without Iterations
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概要
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We present highly accurate least-squares (LS) alternatives to the theoretically optimal maximum likelihood (ML) estimator for homographies between two images. Unlike ML, our estimators are non-iterative and yield solutions even in the presence of large noise. By rigorous error analysis, we derive a “hyperaccurate” estimator which is unbiased up to second order noise terms. Then, we introduce a computational simplification, which we call “Taubin approximation”, without incurring a loss in accuracy. We experimentally demonstrate that our estimators have accuracy surpassing the traditional LS estimator and comparable to the ML estimator.
論文 | ランダム
- ワインのミニチク(97)シャトー・フュイッセ
- ワインのミニチク(96)ジョルジュ・デュブッフ
- ワインのミニチク(94)プリューレ・ロック社(2)
- ワインのミニチク(93)プリューレ・ロック社
- ワインのミニチク(92)アルマン・ルソー社(2)