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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    International journal of computer vision 39 (2000), S. 195-228 
    ISSN: 1573-1405
    Keywords: structure from motion ; alternating minimization ; least-squares ; sphere ; optical flow ; bilinear
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract “Structure From Motion” (SFM) refers to the problem of estimating spatial properties of a three-dimensional scene from the motion of its projection onto a two-dimensional surface, such as the retina. We present an analysis of SFM which results in algorithms that are provably convergent and provably optimal with respect to a chosen norm. In particular, we cast SFM as the minimization of a high-dimensional quadratic cost function, and show how it is possible to reduce it to the minimization of a two-dimensional function whose stationary points are in one-to-one correspondence with those of the original cost function. As a consequence, we can plot the reduced cost function and characterize the configurations of structure and motion that result in local minima. As an example, we discuss two local minima that are associated with well-known visual illusions. Knowledge of the topology of the residual in the presence of such local minima allows us to formulate minimization algorithms that, in addition to provably converge to stationary points of the original cost function, can switch between different local extrema in order to converge to the global minimum, under suitable conditions. We also offer an experimental study of the distribution of the estimation error in the presence of noise in the measurements, and characterize the sensitivity of the algorithm using the structure of Fisher's Information matrix.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2024-04-14
    Description: This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.
    Keywords: System Identification ; Machine Learning ; Linear Dynamical Systems ; Nonlinear Dynamical Systems ; Kernel-based Regularization ; Bayesian Interpretation of Regularization ; Gaussian Processes ; Reproducing Kernel Hilbert Spaces ; Estimation Theory ; Support Vector Machines ; Regularization Networks ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering ; thema EDItEUR::P Mathematics and Science::PH Physics::PHS Statistical physics ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inference ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
    Language: English
    Format: image/jpeg
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