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1. |
Record Nr. |
UNINA9910830622703321 |
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Titolo |
Constitutive modeling of soils and rocks [[electronic resource] /] / edited by Pierre-Yves Hicher, Jian-Fu Shao |
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Pubbl/distr/stampa |
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London, : ISTE |
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Hoboken, NJ, : John Wiley & Sons, 2008 |
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ISBN |
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1-282-25384-0 |
9786613814494 |
0-470-61108-1 |
0-470-39366-1 |
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Descrizione fisica |
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1 online resource (457 p.) |
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Collana |
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Altri autori (Persone) |
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HicherPierre-Yves |
ShaoJian-Fu |
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Disciplina |
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Soggetti |
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Engineering geology - Mathematical models |
Soil mechanics - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"First published in France in 2002 by Hermès Science/Lavoisier entitled 'Modèles de comportement des sols et des roches' ... " --T.p. verso. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Constitutive Modeling of Soils and Rocks; Table of Contents; Preface to the English Edition; Preface to the French; Chapter 1. The Main Classes of Constitutive Relations; 1.1. Introduction; 1.2. The rheological functional; 1.3. Incremental formulation of constitutive relations; 1.4. Rate-independent materials; 1.4.1. Non-linearity of G and H; 1.4.2. Anisotropy of G and H; 1.4.3. Homogenity of degree 1 of G and H; 1.5. Notion of tensorial zones; 1.6. The main classes of rate-independent constitutive relations; 1.6.1. Constitutive relations with one tensorial zone |
1.6.2. Constitutive relations with two tensorial zones1.6.3. Constitutive relations with four tensorial zones; 1.6.4. Constitutive relations with n tensorial zones (n > 4); 1.6.5. Constitutive relations with an infinite number of tensorial zones; 1.6.6. Conclusion; 1.7. The main constitutive relations for rate-dependent materials; 1.7.1. First class of incremental strain decomposition; 1.7.2. Second class of incremental |
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strain decomposition; 1.8. General conclusions; 1.9. References; Chapter 2. Mechanisms of Soil Deformation; 2.1. Introduction; 2.2. Remolded soil behavior |
2.3. Relationships between discontinuous and continuous medium2.3.1. Granular materials; 2.3.2. Remolded clayey materials; 2.3.3. Granular materials with intergranular glue; 2.4. Natural soils; 2.5. Conclusion; 2.6. References; Chapter 3. Elastoplastic Modeling of Soils: Monotonous Loadings; 3.1. Introduction; 3.2. Elastoplasticity equations; 3.2.1. Basic concepts; 3.2.2. Yield surface and elastic domain; 3.2.3. Plastic flow rule; 3.2.4. Incremental relations for one plastic mechanism model; 3.2.5. Incremental relationships for multi-mechanism elastoplasticity |
3.3. Constitutive laws and laboratory tests3.4. Characterization of natural cohesive soil behavior; 3.4.1. Analysis of triaxial test results; 3.4.2. Analysis of oedometer tests; 3.4.3. Elasto-viscoplasticity or elastoplasticity?; 3.5. Characterization of frictional soil behavior; 3.5.1. Analysis of triaxial test results; 3.5.2. Elastoplasticity framework for frictional soils; 3.6. Principles for the derivation of elastoplastic models; 3.6.1. Elastic behavior; 3.6.2. Estimation of the plastic behavior; 3.6.3. Failure surface; 3.6.4. Total and plastic strains; 3.6.5. Plastic potential |
3.6.6. Yield surface3.7. Three-dimensional aspect of the models and calculation of geotechnical works; 3.8. Examples of perfect elastoplastic models; 3.8.1. The Mohr-Coulomb model; 3.8.2. The Drücker-Prager model; 3.9. Examples of elastoplastic models with hardening; 3.9.1. University of Cambridge models (Cam-Clay models); 3.9.2. Nova model (1982 version); 3.9.3. Mélanie model; 3.10. Conclusions; 3.11. Notations; 3.12. References; Chapter 4. Elastoplastic Modeling of Soils: Cyclic Loading; 4.1. Soil behavior under drained loading; 4.1.1. Isotropic and oedometric cyclic loading |
4.1.2. Cyclic triaxial loading |
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Sommario/riassunto |
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This title provides a comprehensive overview of elastoplasticity relating to soil and rocks. Following a general outline of the models of behavior and their internal structure, each chapter develops a different area of this subject relating to the author's particular expertise. The first half of the book concentrates on the elastoplasticity of soft soils and rocks, while the second half examines that of hard soils and rocks. |
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2. |
Record Nr. |
UNINA9910141438903321 |
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Titolo |
Color in computer vision : fundamentals and applications / / Theo Gevers ... [et al.] |
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Pubbl/distr/stampa |
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Hoboken, NJ, : Wiley, c2012 |
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ISBN |
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9786613836229 |
9781283523776 |
1283523779 |
9781118350089 |
1118350081 |
9781118350065 |
1118350065 |
9781118350072 |
1118350073 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (386 p.) |
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Collana |
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Wiley-IS&T Series in Imaging Science and Technology |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Computer vision |
Color vision |
Color photography |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Color in Computer Vision; Contents; Preface; 1 Introduction; 1.1 From Fundamental to Applied; 1.2 Part I: Color Fundamentals; 1.3 Part II: Photometric Invariance; 1.3.1 Invariance Based on Physical Properties; 1.3.2 Invariance By Machine Learning; 1.4 Part III: Color Constancy; 1.5 Part IV: Color Feature Extraction; 1.5.1 From Luminance to Color; 1.5.2 Features, Descriptors, and Saliency; 1.5.3 Segmentation; 1.6 Part V: Applications; 1.6.1 Retrieval and Visual Exploration; 1.6.2 Color Naming; 1.6.3 Multispectral Applications; 1.7 Summary; PART I Color Fundamentals; 2 Color Vision |
2.1 Introduction2.2 Stages of Color Information Processing; 2.2.1 Eye and Optics; 2.2.2 Retina: Rods and Cones; 2.2.3 Ganglion Cells and Receptive Fields; 2.2.4 LGN and Visual Cortex; 2.3 Chromatic Properties |
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of the Visual System; 2.3.1 Chromatic Adaptation; 2.3.2 Human Color Constancy; 2.3.3 Spatial Interactions; 2.3.4 Chromatic Discrimination and Color Deficiency; 2.4 Summary; 3 Color Image Formation; 3.1 Lambertian Reflection Model; 3.2 Dichromatic Reflection Model; 3.3 Kubelka-Munk Model; 3.4 The Diagonal Model; 3.5 Color Spaces; 3.5.1 XYZ System; 3.5.2 RGB System |
3.5.3 Opponent Color Spaces3.5.4 Perceptually Uniform Color Spaces; 3.5.5 Intuitive Color Spaces; 3.6 Summary; PART II Photometric Invariance; 4 Pixel-Based Photometric Invariance; 4.1 Normalized Color Spaces; 4.2 Opponent Color Spaces; 4.3 The HSV Color Space; 4.4 Composed Color Spaces; 4.4.1 Body Reflectance Invariance; 4.4.2 Body and Surface Reflectance Invariance; 4.5 Noise Stability and Histogram Construction; 4.5.1 Noise Propagation; 4.5.2 Examples of Noise Propagation through Transformed Colors; 4.5.3 Histogram Construction by Variable Kernel Density Estimation |
4.6 Application: Color-Based Object Recognition4.6.1 Dataset and Performance Measure; 4.6.2 Robustness Against Noise: Simulated Data; 4.7 Summary; 5 Photometric Invariance from Color Ratios; 5.1 Illuminant Invariant Color Ratios; 5.2 Illuminant Invariant Edge Detection; 5.3 Blur-Robust and Color Constant Image Description; 5.4 Application: Image Retrieval Based on Color Ratios; 5.4.1 Robustness to Illuminant Color; 5.4.2 Robustness to Gaussian Blur; 5.4.3 Robustness to Real-World Blurring Effects; 5.5 Summary; 6 Derivative-Based Photometric Invariance; 6.1 Full Photometric Invariants |
6.1.1 The Gaussian Color Model6.1.2 The Gaussian Color Model by an RGB Camera; 6.1.3 Derivatives in the Gaussian Color Model; 6.1.4 Differential Invariants for the Lambertian Reflection Model; 6.1.5 Differential Invariants for the Dichromatic Reflection Model; 6.1.6 Summary of Full Color Invariants; 6.1.7 Geometrical Color Invariants in Two Dimensions; 6.2 Quasi-Invariants; 6.2.1 Edges in the Dichromatic Reflection Model; 6.2.2 Photometric Variants and Quasi-Invariants; 6.2.3 Relations of Quasi-Invariants with Full Invariants |
6.2.4 Localization and Discriminative Power of Full and Quasi-Invariants |
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Sommario/riassunto |
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While the field of computer vision drives many of today's digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding. Based on the authors' intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, |
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