John Constable et la melancolie du paysage : La campagne anglaise a l'heure romantique / / Thomas Jacquemin ; avec la collaboration d' Anthony Spiegeler

• Record Nr.: UNINA9910812437203321
• Autore: Jacquemin Thomas
• Pubbl/distr/stampa: [Place of publication not identified] : , : 50 Minutes, , 2015
• ISBN: 2-8062-5846-4
• Descrizione fisica: 1 online resource (37 p.)
• Collana: Artistes ; ; Numero 32
• Disciplina: 758.10942
• Soggetti: Landscape painting, English - 19th century
• Lingua di pubblicazione: Francese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Page de titre; John Constable; Contexte; L'Angleterre aux heures républicaines; Les guerres napoléoniennes; Une nouvelle ère de paix; Biographie; Les vertes années; Le grand amour; Un nouveau départ; Le succès et les larmes; Caractéristiques; L'émotion pour unique guide; Peindre sur le vif; La prédominance de la couleur; Sélection d'œuvres; La Vallée de Dedham; Portrait de Maria Bicknell, Madame John Constable; La Charrette à foin; La Cathédrale de Salisbury vue du jardin de l'évêché; Le Château de Hadleigh, l'embouchure de la Tamise, un matin après une nuit d'orage; Cénotaphe à la mémoire de Sir Joshua ReynoldsJohn Constable, une source d'inspiration; En résumé; Pour aller plus loin; Sources bibliographiques; Sources iconographiques; Copyright
• Sommario/riassunto: Décryptez l'art de John Constable en moins d'une heure !Considéré comme l'un des plus grands peintres paysagistes anglais aux côtés de son éternel rival, William Turner, John Constable puise son inspiration dans la campagne anglaise de l'East Anglia. Avec le ciel pour cadre principal de ses toiles, il cherche inlassablement à faire ressortir la beauté et la vitalité de la nature, peignant les reflets de l'eau, les jeux de lumière dans les feuilles des arbres ou encore les mouvements des nuages.Ce livre vous permettra d'en savoir plus sur :- Le contexte socio-politique dans lequel John Constab

Bayesian approach to inverse problems [[electronic resource] /] / edited by Jerome Idier

• Record Nr.: UNINA9911020053003321
• Pubbl/distr/stampa: London, : ISTE; Hoboken, NJ, : John Wiley, c2008
• ISBN: 1-282-16506-2; 9786612165061; 0-470-61119-7; 0-470-39382-3
• Descrizione fisica: 1 online resource (383 p.)
• Collana: Digital signal and image processing series. ; ; v.35
• Altri autori (Persone): IdierJérôme
• Disciplina: 515/.357; 519.542
• Soggetti: Inverse problems (Differential equations); Bayesian statistical decision theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Bayesian Approach to Inverse Problems; Table of Contents; Introduction; Part I. Fundamental Problems and Tools; Chapter 1. Inverse Problems, Ill-posed Problems; 1.1. Introduction; 1.2. Basic example; 1.3. Ill-posed problem; 1.3.1. Case of discrete data; 1.3.2. Continuous case; 1.4. Generalized inversion; 1.4.1. Pseudo-solutions; 1.4.2. Generalized solutions; 1.4.3. Example; 1.5. Discretization and conditioning; 1.6. Conclusion; 1.7. Bibliography; Chapter 2. Main Approaches to the Regularization of Ill-posed Problems; 2.1. Regularization; 2.1.1. Dimensionality control; 2.1.1.1. Truncated singular value decomposition2.1.1.2. Change of discretization; 2.1.1.3. Iterative methods; 2.1.2. Minimization of a composite criterion; 2.1.2.1. Euclidian distances; 2.1.2.2. Roughness measures; 2.1.2.3. Non-quadratic penalization; 2.1.2.4. Kullback pseudo-distance; 2.2. Criterion descent methods; 2.2.1. Criterion minimization for inversion; 2.2.2. The quadratic case; 2.2.2.1. Non-iterative techniques; 2.2.2.2. Iterative techniques; 2.2.3. The convex case; 2.2.4. General case; 2.3. Choice of regularization coefficient; ; 2.3.1. Residual error energy control; 2.3.2. "L-curve" method2.3.3. Cross-validation; 2.4. Bibliography; Chapter 3. Inversion within the Probabilistic Framework; 3.1. Inversion and inference; 3.2. Statistical inference; 3.2.1. Noise law and direct distribution for data; 3.2.2. Maximum likelihood estimation; 3.3. Bayesian approach to inversion; 3.4. Links with deterministic methods; 3.5. Choice of hyperparameters; 3.6. A priori model; 3.7. Choice of criteria; 3.8. The linear, Gaussian case; 3.8.1. Statistical properties of the solution; 3.8.2. Calculation of marginal likelihood; 3.8.3. Wiener filtering; 3.9. Bibliography; Part II. DeconvolutionChapter 4. Inverse Filtering and Other Linear Methods; 4.1. Introduction; 4.2. Continuous-time deconvolution; 4.2.1. Inverse filtering; 4.2.2. Wiener filtering; 4.3. Discretization of the problem; 4.3.1. Choice of a quadrature method; 4.3.2. Structure of observation matrix H; 4.3.3. Usual boundary conditions; 4.3.4. Problem conditioning; 4.3.4.1. Case of the circulant matrix; 4.3.4.2. Case of the Toeplitz matrix; 4.3.4.3. Opposition between resolution and conditioning; 4.3.5. Generalized inversion; 4.4. Batch deconvolution; 4.4.1. Preliminary choices; 4.4.2. Matrix form of the estimate4.4.3. Hunt's method (periodic boundary hypothesis); 4.4.4. Exact inversion methods in the stationary case; 4.4.5. Case of non-stationary signals; 4.4.6. Results and discussion on examples; 4.4.6.1. Compromise between bias and variance in 1D deconvolution; 4.4.6.2. Results for 2D processing; 4.5. Recursive deconvolution; 4.5.1. Kalman filtering; 4.5.2. Degenerate state model and recursive least squares; 4.5.3. Autoregressive state model; 4.5.3.1. Initialization; 4.5.3.2. Criterion minimized by Kalman smoother; 4.5.3.3. Example of result; 4.5.4. Fast Kalman filtering
• Sommario/riassunto: Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise