Eric J. Stollnitz University of Washington |
Tony D. DeRose Pixar Animation Studios |
David H. Salesin University of Washington |
Table of Contents
- Foreword
- Preface
- Notation
- 1 Introduction
- 1.1 Multiresolution methods
1.2 Historical perspective
1.3 Overview of the book
- Part I: Images
- 2 Haar: The simplest wavelet basis
- 2.1 The one-dimensional Haar wavelet transform
2.2 One-dimensional Haar basis functions
2.3 Orthogonality and normalization
2.4 Wavelet compression- 3 Image compression
- 3.1 Two-dimensional Haar wavelet transforms
3.2 Two-dimensional Haar basis functions
3.3 Wavelet image compression
3.4 Color images
3.5 Summary- 4 Image editing
- 4.1 Multiresolution image data structures
4.2 Image editing algorithm
4.3 Boundary conditions
4.4 Display and editing at fractional resolutions
4.5 Image editing examples- 5 Image querying
- 5.1 Image querying by content
5.2 Developing a metric for image querying
5.3 Image querying algorithm
5.4 Image querying examples
5.5 Extensions
- Part II: Curves
- 6 Subdivision curves
- 6.1 Uniform subdivision
6.2 Non-uniform subdivision
6.3 Evaluation masks
6.4 Nested spaces and refinable scaling functions- 7 The theory of multiresolution analysis
- 7.1 Multiresolution analysis
7.2 Orthogonal wavelets
7.3 Semi-orthogonal wavelets
7.4 Biorthogonal wavelets
7.5 Summary- 8 Multiresolution curves
- 8.1 Related curve representations
8.2 Smoothing a curve
8.3 Editing a curve
8.4 Scan conversion and curve compression- 9 Multiresolution tiling
- 9.1 Previous solutions to the tiling problem
9.2 The multiresolution tiling algorithm
9.3 Time complexity
9.4 Tiling examples
- Part III: Surfaces
- 10 Surface wavelets
- 10.1 Overview of multiresolution analysis for surfaces
10.2 Subdivision surfaces
10.3 Selecting an inner product
10.4 A biorthogonal surface wavelet construction
10.5 Multiresolution representations of surfaces- 11 Surface applications
- 11.1 Conversion to multiresolution form
11.2 Surface compression
11.3 Continuous level-of-detail control
11.4 Progressive transmission
11.5 Multiresolution editing
11.6 Future directions for surface wavelets
- Part IV: Physical simulation
- 12 Variational modeling
- 12.1 Setting up the objective function
12.2 The finite-element method
12.3 Using finite elements in variational modeling
12.4 Variational modeling using wavelets
12.5 Adaptive variational modeling- 13 Global illlumination
- 13.1 Radiosity
13.2 Finite elements and radiosity
13.3 Wavelet radiosity
13.4 Enhancements to wavelet radiosity- 14 Further reading
- 14.1 Theory of multiresolution analysis
14.2 Image applications
14.3 Curve and surface applications
14.4 Physical simulation
- Part V: Appendices
- A Linear algebra review
- A.1 Vector spaces
A.2 Bases and dimension
A.3 Inner products and orthogonality
A.4 Norms and normalization
A.5 Eigenvectors and eigenvalues- B B-spline wavelet matrices
- B.1 Haar wavelets
B.2 Endpoint-interpolating linear B-spline wavelets
B.3 Endpoint-interpolating quadratic B-spline wavelets
B.4 Endpoint-interpolating cubic B-spline wavelets- C Matlab code for B-spline wavelets
- Bibliography
- Index
- Color plates
<stoll@amath.washington.edu> | 3:24 pm, 2 January 1997 |