9 of 9 people found the following review helpful:
5.0 out of 5 stars
Excellent, October 14, 2001
This review is from: Principles of Digital Image Synthesis (The Morgan Kaufmann Series in Computer Graphics) 2 Volume Set (Hardcover)
Volume 1:
This book is comprehensive in scope and one of the most well-written technical books in existence. In the preface the author states 'I love to write', and considering the exceptional quality of this book, this indeed shows through.
The first part of the book covers the human visual system, the understanding of which is fundamental to designing effective computer graphics. Several interesting topics are discussed, including Mach bands, color opponency, perceptual color matching, MacAdam ellipses, RGB color space, and gamut mapping.
The second part covers more technical matters, namely that of signal processing. The mathematical background assumed of the reader increases dramatically in this part; some exposure to elementary calculus and differential equations would suffice. The author does a good job of explaining such concepts as linear operators and the Dirac bracket notation. The pictorial representation he gives of the convolution operation is very helpful. In addition, Fourier analysis is presented at a level that makes it very clear exactly what is happening to signals, both discrete and continuous, when taking the Fourier transform. The Fast Fourier transform is not discussed however, dissapointingly. Suprisingly, a whole chapter is devoted to wavelet transforms, a topic usually not included at this level. Wavelets are used as a tool to deal with nonstationary signals. Usually discussed at a very abstract level, the presentation here is crystal clear and vey intutive, and the reader will take away a deeper appreciation of these objects than what could have been obtained from the usual presentations.
Chapter 7 is one of the most important in the book for it covers Monte Carlo techniques for evaluating the integrals that arise in image processing. The speed of convergance of Monte Carlo is addressed, along with how to estimate confidence levels when the parent distribution is normal. The author presents five different ways of doing 'blind' Monte Carlo, including rejection, blind stratified, weighted, and quasi Monte Carlo. Quasi Monte Carlo has taken on particular importance in recent years wherever Monte Carlo techniques are used. The author also presents four different ways of doing 'informed' Monte Carlo, i.e. when some information about the signal is known.
Uniform sampling of continuous signals is done in the next chapter. After discussing an example of sampling and reconstruction, the author outlines in detail the mathematical theory behind the uniform sampling and reconstruction of one-and two-dimensional signals. The chapter ends with a discussion of a technique to reduce aliasing artifacts called supersampling.
The next chapter covers nonuniform sampling and reconstruction. Naturally this is more complicated from a mathematical standpoint, due to the role of stochastic processes, but the author does a good job of discussing the relevant concepts. Most interesting is his treatment of the duality between aliasing and noise.
Chapter 10 surveys some of the more modern and practical techniques used for sampling and reconstruction of two-dimensional signals. Uniform sampling is discussed in terms of rectangular and hexagonal lattices; nonuniform sampling in terms of Poisson sampling and N-books sampling. Pseudocode is given for the decreasing radius algorithm. The concept of a refinement test is introduced and broken down into five categories, each of which is discussed in detail. The refinement test allows one to decide when more samples are needed in a neighborhood, and refinement geometry indicates where the samples are to be placed. Refinement geometry is discussed in this chapter also, with linear and area bisection techniques outlined, along with multiple-level and tree-based sampling. Techniques for interpolation and reconstruction, such as warping are also treated, and the author gives brief overviews of one-dimensional and two-dimensional sampling theorems. Numerous other methods, going by several different names are also discussed.
A very large set of references is given at the end of the book, covering a wide variety of topics in computer graphics and mathematical formalism. I have not read the second volume, but I am sure it respects the high quality of the first.
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3 of 3 people found the following review helpful:
4.0 out of 5 stars
Essential book on image synthesis, but lots of errors, October 10, 2007
This review is from: Principles of Digital Image Synthesis (The Morgan Kaufmann Series in Computer Graphics) 2 Volume Set (Hardcover)
If you read the detailed table of contents for this book, which I provide below since it is not in the product description on this site, it can be quite intimidating. Don't let it be. If you know something about linear algebra at the level of just matrices and vectors and you are famililar with the basic ideas of calculus, even if it has been some time since you actually went through the steps of performing derivatives and integrals, then you can grasp the contents of this book. This is not a book about specific digital imaging tricks. The book sticks to the subject of digital image synthesis, which is converting the description of an image into an image itself. Even on that subject, don't expect a lot of algorithms in numbered steps. This is more of a book about the unified theory of digital image synthesis, showing how the knowledge of physics, numerical methods, signal processing, the human visual system, and even mechanical engineering (fluid flow in particular) all work together to give you insight into how to synthesize images. The only real complaint I have is the extensive errata necessary for reading the book. It is quite annoying to be plowing along, reading and trying to grasp a concept, when I come across an equation that just has to be wrong - and it usually is. The errata is online, however. I suggest you print it out and be prepared to use it often. Glassner is a wonderful writer, and his love and knowledge of the subject shows. For that reason it is worth putting up with the errors to get to the knowledge he has on this subject.
VOLUME I (UNITS I AND II)
I THE HUMAN VISUAL SYSTEM AND COLOR
1 The Human Visual System
1.1 Introduction
1.2 Structure and Optics of the Human Eye
1.3 Spectral and Temporal Aspects of the HVS
1.4 Visual Phenomena
1.4.1 Contrast Sensitivity
1.4.2 Noise
1.4.3 Mach Bands
1.4.4 Lightness Contrast and Constancy
1.5 Depth Perception
1.5.1 Oculomotor Depth
1.5.2 Binocular Depth
1.5.3 Monocular Depth
1.5.4 Motion Parallax
1.6 Color Opponency
1.7 Perceptual Color Matching:; CIE XYZ Space
1.8 Illusions
1.9 Further Reading
1.10 Exercises
2 Color Spaces
2.1 Perceptually Uniform Color Spaces: L*u*v* and L*a*b*
2.2 Other Color Systems
2.3 Further Reading
2.4 Exercises
3 Displays
3.1 Introduction
3.2 CRT Displays
3.3 Display Spot Interaction
3.3.1 Display Spot Profile
3.3.2 Two-Spot Interaction
3.3.3 Display Measurement
3.3.4 Pattern Description
3.3.5 The Uniform Black Field (t = 0)
3.3.6 Clusters of Four (t = .25)
3.3.7 Clusters of Two (t = .5)
3.3.8 The Uniform White Field (t = 1)
3.3.9 Spot Interaction Discussion
3.4 Monitors
3.5 RGB Color Space
3.5.1 Convertin XYZ to Spectra
3.6 Gamut Mapping
3.7 Further Reading
3.8 Exercises
II SIGNAL PROCESSING
4 Signals and Systems
4.1 Introduction
4.2 Types of Signals and Systems
4.2.1 Continuous-Time (CT) Signals
4.2.2 Discrete-Time (DT) Signals
4.2.3 Periodic Signals
4.2.4 Linear Time-Invariant Systems
4.3 Notation
4.3.1 The Real Numbers
4.3.2 The Integers
4.3.3 Intervals
4.3.4 Product Spaces
4.3.5 The Complex Numbers
4.3.6 Assignment and Equality
4.3.7 Summation and Integration
4.3.8 The Complex Exponentials
4.3.9 Braket Notation
4.3.10 Spaces
4.4 Some Useful Signals
4.4.1 The Impulse Signal
4.4.2 The Box Signal
4.4.3 The Impulse Train
4.4.4 The Sinc Signal
4.5 Convolution
4.5.1 A Physical Example of Convulution
4.5.2 The Response of Composite Systems
4.5.3 Eigenfuctions and Frequency Response of LTI Systems
4.5.4 Discrete-Time Convolution
4.6 Two-Dimensional Impulse Response
4.6.1 Linear Systems
4.6.2 Two-Dimensional Impulse Response
4.6.3 Convolution
4.6.4 Two-Dimensional Impulse Response
4.6.5 Eigenfunctions and Frequency Response
4.7 Further Reading
4.8 Exercises
5 Fourier Transforms
5.1 Introduction
5.2 Basis Functions
5.2.1 Projections of Points in Space
5.2.2 Projection of Functions
5.2.3 Orthogonal Families of Functions
5.2.4 The Dual Basis
5.2.5 The Complex Exponential Basis
5.3 Representation in Bases of Lower Dimension
5.4 Continuous-Time Fourier Representations
5.5 The Fourier Series
5.5.1 Convergence
5.6 The Continuous-Time Fouier Transform
5.6.1 Fourier Transform of Periodic Signals
5.6.2 Parseval's Theorem
5.7 Examples
5.7.1 The Box Signal
5.7.2 The Box Specturm
5.7.3 The Guassian
5.7.4 The Impulse Signal
5.7.5 The Impulse Train
5.8 Duality
5.9 Filtering and Convolution
5.9.1 Some Common Filters
5.10 The Fourier Transform Table
5.11 Discrete-Time Fourier Represetnations
5.11.1 The Discrete-Time Fourier Series
5.11.2 The Discrete-Time Fourier Transform
5.12 Fourier Series and Transforms Summary
5.13 Convolution Revisited
5.14 Two-Dimensional Fourier Transforms
5.14.1 Continuous-Time 2D Fourier Transforms
5.14.2 Discrete-Time 2D Fourier Transforms
5.15 Higher-Order Transforms
5.16 The Fast Fourier Transform
5.17 Further Reading
5.18 Exercises
6 Wavelet Transforms
6.1 Introduction
6.2 Short-Time Fourier Transform
6.3 Scale and Resolution
6.4 The Dilation Equation and the Haar Transform
6.5 Decomposition and Reconstruction
6.5.1 Building the Operators
6.6 Compression
6.7 Coefficient Conditions
6.8 Multiresolution Analysis
6.9 Wavelets in the Fourier Domain
6.10 Two-Dimensional Wavelets
6.10.1 The Rectangular Wavelet Decomposition
6.10.2 The Square Wavelet Decomposition
6.11 Further Reading
6.12 Exercises
7 Monte Carlo Integration
7.1 Introduction
7.2 Baisc Monte Carlo Ideas
7.3 Confidence
7.4 Blind Monte Carlo
7.4.1 Crude Monte Carlo
7.4.2 Rejection Monte Carlo
7.4.3 Blind Stratified Sampling
7.4.4 Quasi Monte Carlo
7.4.5 Weighted Monte Carlo
7.4.6 Multidimensional Weighted Monte Carlo
7.5 Informed Monte Carlo
7.5.1 Informed Stratified Sampling
7.5.2 Importance Sampling
7.5.3 Control Variates
7.5.4 Antithetic Variates
7.6 Adaptive Sampling
7.7 Other Approaches
7.8 Summary
7.9 Further Reading
7.10 Exercises
8 Uniform Sampling and Reconstruction
8.1 Introduction
8.1.1 Sampling: Anti-Aliasing in a Pixel
8.1.2 Reconstruction: Evaluating Incident Light at a Point
8.1.3 Outline of this Chapter
8.1.4 Uniform Sampling and Reconstruction of a 1D Continuous Signal
8.1.5 What Signal are Bandlimited?
8.2 Reconstruction
8.2.1 Zero-Order Hold Reconstruction
8.3 Sampling in Two Dimensions
8.4 Two-Dimensional Reconstruction
8.5 Reconstruction in Image Space
8.5.1 The Box Reconstruction Filter
8.5.2 Other Reconstruction Filters
8.6 Supersampling
8.7 Further Reading
8.8 Exercises
9 Nonuniform Sampling and Reconstruction
9.1 Introduction
9.1.1 Variable Sampling Density
9.1.2 Trading Aliasing for Noise
9.1.3 Summary
9.2 Nonuniform Sampling
9.2.1 Adaptive Sampling
9.2.2 Aperiodic Sampling
9.2.3 Sampling Pattern Comparison
9.3 Informed Sampling
9.4 Stratified Sampling
9.4.1 Importance Sampling
9.4.2 Importance and Stratified Sampling
9.5 Interlude: The Duality of Aliasing and Noise
9.6 Nonuniform Reconstruction
9.7 Further REAding
9.8 Exercises
10 Sampling and Reconstruction Techniques
10.1 Introduction
10.2 General Outline of Signal Estimation n
10.3 Initial Sampling Patterns
10.4 Uniform and Nonuniform Sampling
10.5 Initial Sampling
10.5.1 Uniform Sampling
10.5.2 Rectangular Lattice
10.5.3 Hexagonal Lattice
10.5.4 Triangular Lattice
10.5.5 Diamond Lattice
10.5.6 Comparison of Subdivided Hexagonal and Square Lattices
10.5.7 Nonuniform Sampling
10.5.8 Poisson Sampling
10.5.9 N-Rooks Sampling
10.5.10 Jitter Distribution
10.5.11 Poisson-Disk Pattern
10.5.12 Precomputed Poisson-Disk Patterns
10.5.13 Multiple-Scale Poisson-disk Patterns
10.5.14 Sampling Tiles
10.5.15 Dynamic Poisson-Disk Patterns
10.5.16 Importance Sampling
10.5.17 Multidimensional Patterns
10.5.18 Discussion
10.6 Refinement
10.6.1 Sample Intensity
10.7 Refinement Tests
10.7.1 Intensity Comparison Refinement Test
10.7.2 Contrast Refinement Test
10.7.3 Object-Based Refinement Test
10.7.4 Ray-Tree Comparison Refinement Test
10.7.5 Intensity Statistics Refinement Test
10.8 Refinement Sample Geometry
10.9 Refinement Geometry
10.9.1 Linear Bisection
10.9.2 Area Bisection
10.9.3 Nonuniform Geometry
10.9.4 Multiple-Level Sampling
10.9.5 Tree-Based Sampling
10.9.6 Multiple-Scale Template Refinement
10.10 Interpolation and Recontruction
10.10.1 Functional Techniques
10.10.2 Warping
10.10.3 Piecewise-Continuous Recontruction
10.10.5 Local Filtering
10.10.6 Yen's Method
10.10.7 Multistep Reconstruction
10.11 Further Reading
10.12 Exercises Bibiography
Index
VOLUME II (UNITS III, IV, AND V)
III MATTER AND ENERGY
11 Light
11.1 Introduction
11.2 The Double-Slit Experiment
11.3 The Wave Nature of Light
11.4 Polarization
11.5 The Photoelectric Effect
11.6 Particle-Wave Duality
11.7 Reflection and Transmission
11.8 Index of Refraction
11.8.1 Sellmeier's Formula
11.8.2 Cauchy's Formula
11.9 Computing Specular Vectors
11.9.1 The...
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