{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":55,"n_cols":5,"rows_kept":55,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[13,2,13,3],[14,2,14,3],[18,2,18,3],[26,2,26,3],[26,4,26,5],[27,2,27,3],[27,4,27,5],[28,2,28,3],[28,4,28,5],[29,2,29,3],[31,2,31,3],[36,2,36,3],[37,2,37,3],[38,2,38,3],[39,2,39,3],[43,2,43,3],[44,2,44,3],[47,2,47,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910140183603321"]
[null,null,"Titolo",null,"3D video : from capture to diffusion / / edited by  Laurent Lucas, Céline Loscos, Yannick Remion"]
[null,null,"Pubbl/distr/stampa",null,"London ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2013"]
[{"$nulls":4},"©2013"]
[null,null,"ISBN",null,"1-118-76191-X"]
[{"$nulls":4},"1-118-76192-8"]
[{"$nulls":4},"1-118-76186-3"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (482 p.)"]
[null,null,"Collana",null,"Computer engineering series"]
[null,null,"Disciplina",null,"006.696"]
[null,null,"Soggetti",null,"Three-dimensional display systems"]
[{"$nulls":4},"3-D video (Three-dimensional imaging)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Title page; Contents; Foreword; Notations; Acknowledgments; Introduction; PART 1. 3D ACQUISITION OF SCENES; Chapter 1. Foundation; 1.1. Introduction; 1.2. A short history; 1.2.1. The pinhole model; 1.2.2. Depth perception and binocular vision; 1.2.3. Multiview systems; 1.3. Stereopsis and 3D physiological aspects; 1.3.1. Psychophysical indices; 1.3.2. Monocular indices; 1.3.3. Ocular indices; 1.3.4. Binocular indices; 1.4. 3D computer vision; 1.5. Conclusion; 1.6. Bibliography; Chapter 2. Digital Cameras: Definitions and Principles; 2.1. Introduction"]
[{"$nulls":4},"2.2. Capturing light: physical fundamentals2.2.1. Radiometry and photometry; 2.2.2. Wavelengths and color spaces; 2.3. Digital camera; 2.3.1. Optical components; 2.3.2. Electrical components; 2.3.3. Principal functions and their control; 2.3.4. Storage formats for images; 2.4. Cameras, human vision and color; 2.4.1. Adapting optics and electronics to human perception; 2.4.2. Controlling color; 2.5. Improving current performance; 2.5.1. HDR imaging; 2.5.2. Hyperspectral acquisition; 2.6. Conclusion; 2.7. Bibliography; Chapter 3. Multiview Acquisition Systems"]
[{"$nulls":4},"3.1. Introduction: what is a multiview acquisition system?3.2. Binocular systems; 3.2.1. Technical description; 3.2.2. Principal uses; 3.2.3. Related databases; 3.3. Lateral or directional multiview systems; 3.3.1. Technical description; 3.3.2. Principal uses; 3.3.3. Related databases; "]
[{"$nulls":4},"3.4. Global or omnidirectional multiview systems; 3.4.1. Technical description; 3.4.2. Principal uses; 3.4.3. Related databases; 3.5. Conclusion; 3.6. Bibliography; Chapter 4. Shooting and Viewing Geometries in 3DTV; 4.1. Introduction; 4.2. The geometry of 3D viewing; 4.2.1. Description"]
[{"$nulls":4},"4.2.2. Setting the parametric model4.3. The geometry of 3D shooting; 4.3.1. Choosing a convenient geometry; 4.3.2. Setting the parametric model; 4.4. Geometric impact of the 3D workflow; 4.4.1. Rendered-to-shot space mapping; 4.4.2. 3D space distortion model; 4.5. Specification methodology for multiscopic shooting; 4.5.1. Controlling depth distortion; 4.5.2. Accurate depth effect; 4.6. OpenGL implementation; 4.7. Conclusion; 4.8. Bibliography; Chapter 5. Camera Calibration: Geometric and Colorimetric Correction; 5.1. Introduction; 5.2. Camera calibration; 5.2.1. Introduction"]
[{"$nulls":4},"5.2.2. Camera model5.2.3. Calibration using a sight; 5.2.4. Automatic methods; 5.3. Radial distortion; 5.3.1. Introduction; 5.3.2. When to correct distortion?; 5.3.3. Radial distortion correction models; 5.4. Image rectification; 5.4.1. Introduction; 5.4.2. Problems; 5.4.3. Image-based methods; 5.4.4. Camera-based methods; 5.4.5. Correcting more than two images simultaneously; 5.5. Colorimetric considerations in cameras; 5.5.1. Elements of applied colorimetry; 5.5.2. Colorimetric calibration in cameras; 5.6. Conclusion; 5.7. Bibliography; PART 2. DESCRIPTION/RECONSTRUCTION OF 3D SCENES"]
[{"$nulls":4},"Chapter 6. Feature Points Detection and Image Matching"]
[null,null,null,"Sommario/riassunto","While 3D vision has existed for many years, the use of 3D cameras and video-based modeling by the film industry has induced an explosion of interest for 3D acquisition technology, 3D content and 3D displays. As such, 3D video has become one of the new technology trends of this century.The chapters in this book cover a large spectrum of areas connected to 3D video, which are presented both theoretically and technologically, while taking into account both physiological and perceptual aspects. Stepping away from traditional 3D vision, the authors, all currently involved in these areas, prov"]
[null,"2.","Record Nr.",null,"UNINA9910139201603321"]
[null,null,"Autore",null,"Chan Ngai Hang"]
[null,null,"Titolo",null,"Time series : applications to finance with R and S-Plus / / Ngai Hang Chan"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J. : , : Wiley, , 2010"]
[{"$nulls":4},"©2010"]
[null,null,"ISBN",null,"1-280-75948-8"]
[{"$nulls":4},"9786613678010"]
[{"$nulls":4},"1-118-30294-X"]
[{"$nulls":4},"1-118-03246-2"]
[{"$nulls":4},"1-118-03071-0"]
[null,null,"Edizione",null,"[2nd ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xxiii, 296 pages) : illustrations"]
[null,null,"Disciplina",null,"332.01/51955"]
[null,null,"Soggetti",null,"Time-series analysis"]
[{"$nulls":4},"Econometrics"]
[{"$nulls":4},"Risk management"]
[{"$nulls":4},"R (Computer program language)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,null,"Nota di contenuto",null,"Time Series: Applications to Finance with R and S-Plus®, Second Edition; Contents; 12.2.1 Diagonal Form; 12.2.2 Alternative Matrix Form; List of Figures; List of Tables; Preface; Preface to the First Edition; 1 Introduction; 1.1 Basic Description; 1.2 Simple Descriptive Techniques; 1.2.1 Trends; 1.2.2 Seasonal Cycles; 1.3 Transformations; 1.4 Example; 1.5 Conclusions; 1.6 Exercises; 2 Probability Models; 2.1 Introduction; 2.2 Stochastic Processes; 2.3 Examples; 2.4 Sample Correlation Function; 2.5 Exercises; 3 Autoregressive Moving Average Models; 3.1 Introduction; 3.2 Moving Average Models"]
[{"$nulls":4},"3.3 Autoregressive Models; 3.3.1 Duality between Causality and Stationarity*; 3.3.2 Asymptotic Stationarity; 3.3.3 Causality Theorem; 3.3.4 Covariance Structure of AR Models; 3.4 ARMA Models; 3.5 ARIMA Models; 3.6 Seasonal ARIMA; 3.7 Exercises; 4 Estimation in the Time Domain; 4.1 Introduction; 4.2 Moment Estimators; 4.3 Autoregressive Models; 4.4 Moving Average Models; 4.5 ARMA Models; 4.6 Maximum Likelihood Estimates; 4.7 Partial ACF; 4.8 Order Selections*; 4.9 Residual Analysis; 4.10 Model Building; 4.11 Exercises; 5 Examples in "]
[{"$nulls":4},"SPLUS and R; 5.1 Introduction; 5.2 Example 1; 5.3 Example 2"]
[{"$nulls":4},"5.4 Exercises; 6 Forecasting; 6.1 Introduction; 6.2 Simple Forecasts; 6.3 Box and Jenkins Approach; 6.4 Treasury Bill Example; 6.5 Recursions*; 6.6 Exercises; 7 Spectral Analysis; 7.1 Introduction; 7.2 Spectral Representation Theorems; 7.3 Periodogram; 7.4 Smoothing of Periodogram*; 7.5 Conclusions; 7.6 Exercises; 8 Nonstationarity; 8.1 Introduction; 8.2 Nonstationarity in Variance; 8.3 Nonstationarity in Mean: Random Walk with Drift; 8.4 Unit Root Test; 8.5 Simulations; 8.6 Exercises; 9 Heteroskedasticity; 9.1 Introduction; 9.2 ARCH; 9.3 GARCH; 9.4 Estimation and Testing for ARCH"]
[{"$nulls":4},"9.5 Example of Foreign Exchange Rates; 9.6 Exercises; 10 Multivariate Time Series; 10.1 Introduction; 10.2 Estimation of μ and Γ; 10.3 Multivariate ARMA Processes; 10.3.1 Causality and Invertibility; 10.3.2 Identifiability; 10.4 Vector AR Models; 10.5 Example of Inferences for VAR; 10.6 Exercises; 11 State Space Models; 11.1 Introduction; 11.2 State Space Representation; 11.3 Kalman Recursions; 11.4 Stochastic Volatility Models; 11.5 Example of Kalman Filtering of Term Structure; 11.6 Exercises; 12 Multivariate GARCH; 12.1 Introduction; 12.2 General Model; 12.3 Quadratic Form"]
[{"$nulls":4},"12.3.1 Single-Factor GARCH(1,1); 12.3.2 Constant-Correlation Model; 12.4 Example of Foreign Exchange Rates; 12.4.1 The Data; 12.4.2 Multivariate GARCH in SPLUS; 12.4.3 Prediction; 12.4.4 Predicting Portfolio Conditional Standard Deviations; 12.4.5 BEKK Model; 12.4.6 Vector-Diagonal Models; 12.4.7 ARMA in Conditional Mean; 12.5 Conclusions; 12.6 Exercises; 13 Cointegrations and Common Trends; 13.1 Introduction; 13.2 Definitions and Examples; 13.3 Error Correction Form; 13.4 Granger's Representation Theorem; 13.5 Structure of Cointegrated Systems; 13.6 Statistical Inference for Cointegrated Systems"]
[null,null,null,"Sommario/riassunto","\"This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds. The book offers succinct coverage of standard topics in statistical time series - such as forecasting and spectral analysis - in a manner that is both technical and conceptual. Recent developments in nonstandard time series techniques such as Bayesian methods and arbitrage statistics have been added to this edition, and they are illustrated in detail with real financial examples. Subroutines in R and S-Plus are lavishly displayed throughout in this new edition. An author website provides instructor notations and additional software subroutines, as well as complete solutions to the exercises in the text.\"--"]
[{"$nulls":4},"\"This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds\"--"]