منابع مشابه
Geometric Data Fitting
Given a dense set of points lying on or near an embedded submanifold M0 ⊂ Rn of Euclidean space, the manifold fitting problem is to find an embedding F :M → Rn that approximatesM0 in the sense of least squares. When the dataset is modeled by a probability distribution, the fitting problem reduces to that of finding an embedding that minimizes Ed[F], the expected square of the distance from a po...
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Contents Preface v Chapter 1. Introduction and historic overview 1 1.1. Classical regression 1 1.2. Errors-in-variables (EIV) model 3 1.3. Geometric fit 5 1.4. Solving a general EIV problem 8 1.5. Non-linear nature of the 'linear' EIV 11 1.6. Statistical properties of the orthogonal fit 13 1.7. Relation to total least squares (TLS) 15 1.8. Nonlinear models 16 1.9. Notation and preliminaries 17 ...
متن کاملHyperaccuracy for Geometric Fitting
A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric models from noisy data. It is first pointed out that parameter estimation for computer vision applications is very different in nature from traditional statistical analysis and that a different mathematical framework is necessary in such a domain. After general theories on estimation and accuracy ...
متن کاملPerformance evaluation of iterative geometric fitting algorithms
The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well known fitting algorithms are described: FNS, HEIV, and renormalization. To these, we add a special variant of Gauss-Newton iterations. For initialization of iterations, random choice, least...
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ژورنال
عنوان ژورنال: Abstract and Applied Analysis
سال: 2004
ISSN: 1085-3375
DOI: 10.1155/s1085337504401043