LEADER 01099nam0-22003851i-450- 001 990003202910403321 005 20070108122945.0 010 $a0-444-87529-8 035 $a000320291 035 $aFED01000320291 035 $a(Aleph)000320291FED01 035 $a000320291 100 $a20030910d1985----km-y0itay50------ba 101 0 $aeng 102 $aIT 200 1 $aStabilization Policy in France and the Federal Republic of Germany$fedited by G.De Ménil and U.Westphal 210 $aAmsterdam$cNorth-Holland$d1985 215 $aXII, 379 p.$d21 cm 225 1 $aContributions to economic analysis$v153 610 0 $aFrancia$aStoria 676 $aB/1.0 676 $aF/1.421 676 $aF/1.424 676 $aN/1.4 702 1$aDe Ménil,$bGeorge 702 1$aWestphal,$bUwe 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003202910403321 952 $aN/14 STA$b6563$fSES 952 $aE6.12$b8627$fDECTS 959 $aSES 959 $aDECTS 996 $aStabilization Policy in France and the Federal Republic of Germany$9454471 997 $aUNINA LEADER 01101nam0-22003371i-450- 001 990002577910403321 005 20040120134736.0 010 $a3540171665 035 $a000257791 035 $aFED01000257791 035 $a(Aleph)000257791FED01 035 $a000257791 100 $a20030910d--------km-y0itay50------ba 101 0 $aeng 200 1 $aStochastic processes in classical and quantum systems$eproceedings of the 1. Ascona-Como international conference, held in Ascona, Ticino (Switzerlad), june 24-29, 1985$fedited by S. Albeverio, G. Casati, D. Merlini 210 $aBerlin$cSpringer Verlag$d[1985] 215 $axi, 550 p.$d24 cm 225 1 $aLecture notes in physics$v262 610 0 $aAtti di convegni 610 0 $aProcessi stocastici 676 $a530 702 1$aAlbeverio,$bSergio 702 1$aCasati,$bG. 702 1$aMerlini,$bD. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002577910403321 952 $aMXXXI-A-134$b851$fMAS 959 $aMAS 996 $aStochastic processes in classical and quantum systems$9436033 997 $aUNINA LEADER 03330nam 2200697Ia 450 001 9910145261603321 005 20210209181231.0 010 $a1-119-96447-4 010 $a1-282-03422-7 010 $a9786612034220 010 $a0-470-69972-8 010 $a0-470-71444-1 035 $a(CKB)1000000000707296 035 $a(EBL)416352 035 $a(OCoLC)437096635 035 $a(SSID)ssj0000182339 035 $a(PQKBManifestationID)11181417 035 $a(PQKBTitleCode)TC0000182339 035 $a(PQKBWorkID)10167021 035 $a(PQKB)10949098 035 $a(MiAaPQ)EBC416352 035 $a(MiAaPQ)EBC4041335 035 $a(Au-PeEL)EBL4041335 035 $a(CaPaEBR)ebr11114237 035 $a(CaONFJC)MIL203422 035 $a(OCoLC)927509010 035 $a(PPN)152375090 035 $a(EXLCZ)991000000000707296 100 $a20080721d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn introduction to 3D computer vision techniques and algorithms$b[electronic resource] /$fBogus?aw Cyganek, J. Paul Siebert 205 $a1st ed. 210 $aChichester, U.K. $cJohn Wiley & Sons$d2009 215 $a1 online resource (514 p.) 300 $aDescription based upon print version of record. 311 $a0-470-01704-X 320 $aIncludes bibliographical references (p. [459]-474) and index. 327 $aAN INTRODUCTION TO 3D COMPUTER VISION TECHNIQUES AND ALGORITHMS; Contents; Preface; Acknowledgements; Notation and Abbreviations; Part I; 1 Introduction; 2 Brief History of Research on Vision; Part II; 3 2D and 3D Vision Formation; 4 Low-level Image Processing for Image Matching; 5 Scale-space Vision; 6 Image Matching Algorithms; 7 Space Reconstruction and Multiview Integration; 8 Case Examples; Part III; 9 Basics of the Projective Geometry; 10 Basics of Tensor Calculus for Image Processing; 11 Distortions and Noise in Images; 12 Image Warping Procedures 327 $a13 Programming Techniques for Image Processing and Computer Vision14 Image Processing Library; References; Index; Colorplate 330 $aComputer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D com 606 $aComputer vision 606 $aThree-dimensional imaging 606 $aComputer algorithms 615 0$aComputer vision. 615 0$aThree-dimensional imaging. 615 0$aComputer algorithms. 676 $a006.3/7 676 $a006.37 700 $aCyganek$b Bogus?aw$0890978 701 $aSiebert$b J. Paul$0732402 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145261603321 996 $aAn introduction to 3D computer vision techniques and algorithms$91990216 997 $aUNINA LEADER 05442nam 2200673Ia 450 001 9910139593803321 005 20170809165509.0 010 $a1-283-27370-5 010 $a9786613273703 010 $a1-118-02916-X 010 $a1-118-02917-8 010 $a1-118-02915-1 035 $a(CKB)2550000000054366 035 $a(EBL)697550 035 $a(OCoLC)757486955 035 $a(SSID)ssj0000550637 035 $a(PQKBManifestationID)11337043 035 $a(PQKBTitleCode)TC0000550637 035 $a(PQKBWorkID)10509693 035 $a(PQKB)11585926 035 $a(MiAaPQ)EBC697550 035 $a(PPN)170261689 035 $a(EXLCZ)992550000000054366 100 $a20101105d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApproximate dynamic programming$b[electronic resource] $esolving the curses of dimensionality /$fWarren B. Powell 205 $a2nd ed. 210 $aHoboken, N.J. $cJ. Wiley & Sons$dc2011 215 $a1 online resource (658 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-60445-X 320 $aIncludes bibliographical references and index. 327 $aApproximate Dynamic Programming; Contents; Preface to the Second Edition; Preface to the First Edition; Acknowledgments; 1 The Challenges of Dynamic Programming; 1.1 A Dynamic Programming Example: A Shortest Path Problem; 1.2 The Three Curses of Dimensionality; 1.3 Some Real Applications; 1.4 Problem Classes; 1.5 The Many Dialects of Dynamic Programming; 1.6 What Is New in This Book?; 1.7 Pedagogy; 1.8 Bibliographic Notes; 2 Some Illustrative Models; 2.1 Deterministic Problems; 2.2 Stochastic Problems; 2.3 Information Acquisition Problems; 2.4 A Simple Modeling Framework for Dynamic Programs 327 $a2.5 Bibliographic NotesProblems; 3 Introduction to Markov Decision Processes; 3.1 The Optimality Equations; 3.2 Finite Horizon Problems; 3.3 Infinite Horizon Problems; 3.4 Value Iteration; 3.5 Policy Iteration; 3.6 Hybrid Value-Policy Iteration; 3.7 Average Reward Dynamic Programming; 3.8 The Linear Programming Method for Dynamic Programs; 3.9 Monotone Policies*; 3.10 Why Does It Work?**; 3.11 Bibliographic Notes; Problems; 4 Introduction to Approximate Dynamic Programming; 4.1 The Three Curses of Dimensionality (Revisited); 4.2 The Basic Idea; 4.3 Q-Learning and SARSA 327 $a4.4 Real-Time Dynamic Programming4.5 Approximate Value Iteration; 4.6 The Post-Decision State Variable; 4.7 Low-Dimensional Representations of Value Functions; 4.8 So Just What Is Approximate Dynamic Programming?; 4.9 Experimental Issues; 4.10 But Does It Work?; 4.11 Bibliographic Notes; Problems; 5 Modeling Dynamic Programs; 5.1 Notational Style; 5.2 Modeling Time; 5.3 Modeling Resources; 5.4 The States of Our System; 5.5 Modeling Decisions; 5.6 The Exogenous Information Process; 5.7 The Transition Function; 5.8 The Objective Function; 5.9 A Measure-Theoretic View of Information** 327 $a5.10 Bibliographic NotesProblems; 6 Policies; 6.1 Myopic Policies; 6.2 Lookahead Policies; 6.3 Policy Function Approximations; 6.4 Value Function Approximations; 6.5 Hybrid Strategies; 6.6 Randomized Policies; 6.7 How to Choose a Policy?; 6.8 Bibliographic Notes; Problems; 7 Policy Search; 7.1 Background; 7.2 Gradient Search; 7.3 Direct Policy Search for Finite Alternatives; 7.4 The Knowledge Gradient Algorithm for Discrete Alternatives; 7.5 Simulation Optimization; 7.6 Why Does It Work?**; 7.7 Bibliographic Notes; Problems; 8 Approximating Value Functions; 8.1 Lookup Tables and Aggregation 327 $a8.2 Parametric Models8.3 Regression Variations; 8.4 Nonparametric Models; 8.5 Approximations and the Curse of Dimensionality; 8.6 Why Does It Work?**; 8.7 Bibliographic Notes; Problems; 9 Learning Value Function Approximations; 9.1 Sampling the Value of a Policy; 9.2 Stochastic Approximation Methods; 9.3 Recursive Least Squares for Linear Models; 9.4 Temporal Difference Learning with a Linear Model; 9.5 Bellman's Equation Using a Linear Model; 9.6 Analysis of TD(0), LSTD, and LSPE Using a Single State; 9.7 Gradient-Based Methods for Approximate Value Iteration* 327 $a9.8 Least Squares Temporal Differencing with Kernel Regression* 330 $aPraise for the First Edition ""Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners.""-Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems i 410 0$aWiley series in probability and statistics. 606 $aDynamic programming 606 $aProgramming (Mathematics) 615 0$aDynamic programming. 615 0$aProgramming (Mathematics) 676 $a519.7/03 676 $a519.703 700 $aPowell$b Warren B.$f1955-$0882830 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139593803321 996 $aApproximate dynamic programming$91972218 997 $aUNINA