Pixels & Paintings - David G Stork

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Résumé :

List of Figures xxi

List of Tables xlv

List of Algorithms xlvii

Preface xlix

Lorenzo Lotto lviii

Giovanni Morelli and the birth of scientific connoisseurship lix

Overview lxi

Intended audience lxii

Prerequisites lxiii

Acknowledgements lxiv

1 Digital imaging 1

1.1 Introduction 1

1.2 Electromagnetic radiation and light 4

1.3 Interaction of electromagnetic radiation with art materials 7

1.4 Cameras and scanners 9

1.4.1 Cameras 10

1.4.2 Flatbed scanners 11

1.5 Parameters for image acquisition in the visible 12

Billy Pappas 13

1.5.1 Spatial resolution 15

1.5.2 Bit depth 16

1.5.3 Dynamic range and contrast 17

1.6 Reading digital images of art on-screen 18

1.6.1 Reading a digital image of Leonardo's La Bella Principessa 22

Leonardo da Vinci 22

1.7 Infrared photography and reflectography 25

1.8 Ultraviolet imaging 26

1.9 Multispectral and hyperspectral imaging 27

1.9.1 Hyperspectral imaging of the Archimedes Palimpsest 30

1.10 X-radiographic imaging 32

1.11 Fluorescence imaging 35

1.12 Capture of three-dimensional surfaces of art 37

1.12.1 Raking illumination 38

1.12.2 Reflectance transformation imaging (RTI) 40

1.12.3 Stereographic imaging 42

1.13 Optical coherence tomography (OCT) 43

1.14 Raman spectroscopic imaging and X-ray fluorescence imaging 45

1.14.1 Raman spectroscopic imaging (RSI) 45

1.14.2 X-ray fluorescence imaging (XRF) 46

1.15 Summary 47

1.16 Bibliographical remarks 49

2 Image processing 53

2.1 Introduction 53

2.2 Pixel-based image processing 57

2.3 Region-based image processing 61

2.3.1 Linear image processing 62

2.3.2 Nonlinear region-based image processing 63

2.3.3 Color quantization 64

2.3.4 Edge and line detection 69

2.3.5 Dilation and erosion 71

2.3.6 Skeletonization 72

2.4 Inpainting 72

2.5 Feature extraction 74

2.5.1 Keypoint extraction 75

2.5.2 Craquelure and crazing analysis 78

2.5.3 Computational tests for counterproofing by Jan van der Heyden 81

Jan van der Heyden 83

2.6 Segmentation 86

2.6.1 Deep nets for image segmentation 88

2.7 Geometric transformations 95

2.8 Chamfer transform and Chamfer distance 101

2.8.1 Tests for copying of Jan van Eyck's portraits of Niccol? Albergati 103

2.9 Discrete Fourier and wavelet transforms 111

2.9.1 Discrete Fourier transform (DFT) 111

2.9.2 Canvas support weave analysis 114

2.9.3 Discrete wavelet transform (DWT) 116

2.10 Compositing and integrating art images 118

2.10.1 Image compositing 118

2.10.2 Superresolution 119

2.11 Image separation 123

2.12 Summary 123

2.13 Bibliographical remarks 125

3 Color analysis 129

3.1 Introduction 129

3.2 Visible-light spectra and color appearance 132

3.3 Overview of human color vision 133

3.3.1 Properties of color descriptions 134

3.3.2 Opponent color processing and unique hues 137

3.3.3 Humanist descriptions of color 138

3.3.4 Spatial aspects of color perception 139

Josef Albers 140

3.3.5 Color and lightness constancy and brightness perception 141

3.3.6 Quantitative descriptions and additive color mixing 141

3.3.7 Representing artists' palettes 145

3.4 Physics of color in art materials 147

3.4.1 Pigments and color appearance 147

3.5 Representing color arising from mixing paints 151

3.5.1 Identifying pigments in artworks ...

Sommaire:

Dr. David G. Stork is a graduate of MIT and the University of Maryland and studied art history at Wellesley College. He is an Adjunct Professor at Stanford University. Dr. Stork holds 64 U.S. patents and has published over 220 peer-reviewed scholarly works in machine learning, pattern recognition, computational optics, and image understanding of art. His many books include Seeing the Light, Pattern Classification Second Edition, and HAL's Legacy. He is a Fellow of IEEE, OSA, SPIE, IS&T, IAPR, IARIA, and AAIA, and a 2023 Leonardo@ Djerassi Fellow....