Ballard D., Computer Vision (2nd ed.).pdf

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Computer Vision: Table of Contents
Table of Contents
Preface
Original Table of Contents
1 -
Computer vision issues
1.1 -
Achieving simple vision goals
(pg 1)
1.2 -
High-level and low-level capabilities
(pg 2)
1.3 -
A range of representations
(pg 6)
1.4 -
The role of computers
(pg 9)
1.5 -
Computer vision research and applications
(pg 12)
2 -
Image Formation
2.1 -
Images
(pg 4)
2.2 -
Image Model
(pg 1)
2.2.1 -
Image Functions
(pg 1)
2.2.2 -
Imaging Geometry
(pg 2)
2.2.3 -
Reflectance
(pg 5)
2.2.4 -
Spatial Properties
(pg 7)
2.2.5 -
Color
(pg 14)
2.2.6 -
Digital Images
(pg 18)
2.3 -
Imaging Devices for Computer Vision
(pg 1)
2.3.1 -
Photographic Imaging
(pg 3)
2.3.2 -
Sensing Range
(pg 11)
2.3.3 -
Reconstruction Imaging
(pg 15)
3 -
Early Processing
3.1 -
Recovering Intrinsic Structure
(pg 1)
3.2 -
Filtering the Image
(pg 3)
3.2.1 -
Template Matching
(pg 3)
3.2.2 -
Histogram Transformations
(pg 8)
3.2.3 -
Background Subtraction
(pg 10)
3.2.4 -
Filtering and Reflectance Models
(pg 11)
3.3 -
Finding Local Edges
(pg 1)
3.3.1 -
Types of Edge Operators
(pg 2)
3.3.2 -
Edge Thresholding Strategies
(pg 6)
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Computer Vision: Table of Contents
3.3.3 -
Three-Dimensional Edge Operators
(pg 7)
3.3.4 -
How Good Are Edge Operators?
(pg 9)
3.3.5 -
Edge Relaxation
(pg 11)
3.4 -
Range Information from Geometry
(pg 1)
3.4.1 -
Stereo Vision and Triangulation
(pg 1)
3.4.2 -
A Relaxation Algorithm for Stereo
(pg 2)
3.5 -
Surface Orientation from Reflectance Models
(pg 6)
3.5.1 -
Reflectivity Functions
(pg 6)
3.5.2 -
Surface Gradient
(pg 8)
3.5.3 -
Photometric Stereo
(pg 11)
3.5.4 -
Shape from Shading by Relaxation
(pg 12)
3.6 -
Optical Flow
(pg 1)
3.6.1 -
The Fundamental Flow Constraint
(pg 1)
3.6.2 -
Calculating Optical Flow by Relaxation
(pg 2)
3.7 -
Resolution Pyramids
(pg 5)
3.7.1 -
Gray-Level Consolidation
(pg 5)
3.7.2 -
Pyramidal Structures in Correlation
(pg 6)
3.7.3 -
Pyramidal Structures in Edge Detection
(pg 8)
4 -
Boundary Detection
4.1 -
On Associating Edge Elements
(pg 4)
4.2 -
Searching Near an Approximate Location
(pg 6)
4.2.1 -
Adjusting A Priori Boundaries
(pg 6)
4.2.2 -
Non-Linear Correlation in Edge Space
(pg 6)
4.2.3 -
Divide-and-Conquer Boundary Detection
(pg 7)
4.3 -
The Hough Method for Curve Detection
(pg 1)
4.3.1 -
Use of the Gradient
(pg 2)
4.3.2 -
Some Examples
(pg 3)
4.3.3 -
Trading Off Work in Parameter Space for Work in Image Space
(pg 4)
4.3.4 -
Generalizing the Hough Transform
(pg 6)
4.4 -
Edge Following as Graph Searching
(pg 1)
4.4.1 -
Good Evaluation Functions
(pg 3)
4.4.2 -
Finding All the Boundaries
(pg 3)
4.4.3 -
Alteratives to the A Algorithm
(pg 6)
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Computer Vision: Table of Contents
4.5 -
Edge Following as Dynamic Programming
(pg 1)
4.5.1 -
Dynamic Programming
(pg 1)
4.5.2 -
Dynamic Programming for Images
(pg 3)
4.5.3 -
Lower Resolution Evaluation Functions
(pg 5)
4.5.4 -
Theoretical Questions about Dynamic Programming
(pg 7)
4.6 -
Contour Following
(pg 7)
4.6.1 -
Extension to Gray-Level Images
(pg 8)
4.6.2 -
Generalization to Higher-Dimensional Image Data
(pg 10)
5 -
Region Growing
5.1 -
Regions
(pg 1)
5.2 -
A Local Technique: Blob Coloring
(pg 3)
5.3 -
Global Techniques: Region Growing via Thresholding
(pg 4)
5.3.1 -
Thresholding in Multidimensional Space
(pg 5)
5.3.2 -
Hierarchical Refinement
(pg 7)
5.4 -
Splitting and Merging
(pg 7)
5.4.1 -
State-Space Approach to Region Growing
(pg 9)
5.4.2 -
Low-Level Boundary Data Structures
(pg 10)
5.4.3 -
Graph-Oriented Region Structures
(pg 11)
5.5 -
Incorporation of Semantics
(pg 12)
6 -
Texture
6.1 -
What is Texture?
(pg 1)
6.2 -
Texture Primitives
(pg 4)
6.3 -
Structural Models of Texel Placement
(pg 5)
6.3.1 -
Grammatical Models
(pg 7)
6.3.2 -
Shape Grammars
(pg 8)
6.3.3 -
Tree Grammars
(pg 10)
6.3.4 -
Array Grammars
(pg 13)
6.4 -
Texture as a Pattern Recognition Problem
(pg 1)
6.4.1 -
Texture Energy
(pg 4)
6.4.2 -
Spatial Gray-Level Dependence
(pg 6)
6.4.3 -
Region Texels
(pg 8)
6.5 -
The Texture Gradient
(pg 9)
7 -
Motion
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Computer Vision: Table of Contents
7.1 -
Motion Understanding
(pg 1)
7.1.1 -
Domain-Independent Understanding
(pg 2)
7.1.2 -
Domain-Dependent Understanding
(pg 2)
7.2 -
Understanding Optical Flow
(pg 5)
7.2.1 -
Focus of Expansion
(pg 5)
7.2.2 -
Adjacency, Depth, and Collision
(pg 7)
7.2.3 -
Surface Orientation and Edge Detection
(pg 8)
7.2.4 -
Egomotion
(pg 12)
7.3 -
Understanding Image Sequences
(pg 1)
7.3.1 -
Calculating Flow from Discrete Images
(pg 1)
7.3.2 -
Rigid Bodies from Motion
(pg 4)
7.3.3 -
Interpretation of Moving Light Displays - A Domain-Independent Approach
(pg 8)
7.3.4 -
Human Motion Understanding - A Model-Directed Approach
(pg 11)
7.3.5 -
Segmented Images
(pg 14)
8 -
Representation of Two-Dimensional Geometric Structures
8.1 -
Two-Dimensional Geometric Structures
(pg 4)
8.2 -
Boundary Representations
(pg 5)
8.2.1 -
Polylines
(pg 5)
8.2.2 -
Chain Codes
(pg 8)
8.2.3 -
The Ψ-s Curve
(pg 10)
8.2.4 -
Fourier Descriptors
(pg 11)
8.2.5 -
Conic Sections
(pg 12)
8.2.6 -
B-Splines
(pg 12)
8.2.7 -
Strip Trees
(pg 17)
8.3 -
Region Representations
(pg 1)
8.3.1 -
Spatial Occupancy Array
(pg 1)
8.3.2 -
y
Axis
(pg 2)
8.3.3 -
Quad Trees
(pg 3)
8.3.4 -
Medial Axis Transform
(pg 6)
8.3.5 -
Decomposing Complex Areas
(pg 7)
8.4 -
Simple Shape Properties
(pg 8)
8.4.1 -
Area
(pg 8)
8.4.2 -
Eccentricity
(pg 9)
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Computer Vision: Table of Contents
8.4.3 -
Euler Number
(pg 9)
8.4.4 -
Compactness
(pg 10)
8.4.5 -
Slope Density Function
(pg 10)
8.4.6 -
Signatures
(pg 11)
8.4.7 -
Concavity Trees
(pg 12)
8.4.8 -
Shape Numbers
(pg 12)
9 -
Representations of Three-Dimensional Structures
9.1 -
Solids and their Representation
(pg 1)
9.2 -
Surface Representations
(pg 2)
9.2.1 -
Surface with Faces
(pg 2)
9.2.2 -
Surfaces Based on Splines
(pg 5)
9.2.3 -
Surfaces That Are Functions on the Sphere
(pg 7)
9.3 -
Generalized Cylinder Representations
(pg 1)
9.3.1 -
Generalized Cylinder Coordinate Systems and Properties
(pg 2)
9.3.2 -
Extracting Generalized Cylinders
(pg 5)
9.3.3 -
A Discrete Volumetric Version of the Skeleton
(pg 7)
9.4 -
Volumetric Representations
(pg 7)
9.4.1 -
Spatial Occupancy
(pg 7)
9.4.2 -
Cell Decomposition
(pg 8)
9.4.3 -
Constructive Solid Geometry
(pg 9)
9.4.4 -
Algorithms for Solid Representations
(pg 11)
9.5 -
Understanding Line Drawings
(pg 1)
9.5.1 -
Matching Line Drawings to Three-Dimensional Primitives
(pg 3)
9.5.2 -
Grouping Regions Into Bodies
(pg 4)
9.5.3 -
Labeling Lines
(pg 6)
9.5.4 -
Reasoning About Planes
(pg 11)
10 -
Knowledge Representation and Use
10.1 -
Representations
(pg 4)
10.1.1 -
The Knowledge Base - Models and Processes
(pg 5)
10.1.2 -
Analogical and Propositional Representations
(pg 6)
10.1.3 -
Procedural Knowledge
(pg 8)
10.1.4 -
Computer Implementations
(pg 9)
10.2 -
Semantic Nets
(pg 1)
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