Computer Vision

ISBN-10: 0130307963

ISBN-13: 9780130307965

Edition: 2001

Authors: George Stockman, Linda G. Shapiro

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This text provides theory and examples for those working in fields where significant information must be extracted automatically from images. It provides a basic set of fundamental concepts and algorithms for analyzing images.
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Book details

List price: $220.20
Copyright year: 2001
Publisher: Prentice Hall PTR
Publication date: 1/23/2001
Binding: Paperback
Pages: 608
Size: 7.00" wide x 9.25" long x 1.00" tall
Weight: 2.288
Language: English

Machines that See?
Application Problems
A Preview of the Digital Image
Image Database Query
Inspecting Crossbars for Holes
Examining the Inside of a Human Head
Processing Scanned Text Pages
Accounting for Snow Cover Using a Satellite Image
Understanding a Scene of Parts
Operations on Images
Changing Pixels in Small Neighborhoods
Enhancing an Entire Image
Combining Multiple Images
Computing Features from an Image
Extracting Non-iconic Representations
The Good, the Bad, and the Ugly
Use of Computers and Software
Related Areas
The Rest of the Book
Additional Exercises
Imaging and Image Representation
Sensing Light
Imaging Devices
CCD Cameras
Image Formation
Video Cameras
The Human Eye
Problems in Digital Images*
Geometric Distortion
CCD Variations
Clipping or Wrap-Around
Chromatic Distortion
Quantization Effects
Picture Functions and Digital Images
Types of Images
Image Quantization and Spatial Measurement
Digital Image Formats*
Image File Header
Image Data
Data Compression
Commonly Used Formats
Run-Coded Binary Images
PGM: Portable Gray Map
GIF Image File Format
TIFF Image File Format
JPEG Format for Still Photos
MPEG Format for Video
Comparison of Formats
Richness and Problems of Real Imagery
3D Structure from 2D Images
Five Frames of Reference
Pixel Coordinate Frame I
Object Coordinate Frame O
Camera Coordinate Frame C
Real Image Coordinate Frame F
World Coordinate Frame W
Other Types of Sensors*
Color and Multispectral Images*
Magnetic Resonance Imaging (MRI)*
Range Scanners and Range Images*
Binary Image Analysis
Pixels and Neighborhoods
Applying Masks to Images
Counting the Objects in an Image
Connected Components Labeling
Binary Image Morphology
Structuring Elements
Basic Operations
Some Applications of Binary Morphology
Conditional Dilation
Region Properties
Region Adjacency Graphs
Thresholding Gray-Scale Images
The Use of Histograms for Threshold Selection
Automatic Thresholding: The Otsu Method*
Pattern Recognition Concepts
Pattern Recognition Problems
Common Model for Classification
Feature Extractor
Building the Classification System
Evaluation of System Error
False Alarms and False Dismissals
Precision Versus Recall
Features Used for Representation
Feature Vector Representation
Implementing the Classifier
Classification Using the Nearest Class Mean
Classification Using the Nearest Neighbors
Structural Techniques
The Confusion Matrix
Decision Trees
Bayesian Decision-Making
Parametric Models for Distributions
Decisions Using Multidimensional Data
Machines that Learn
Artificial Neural Nets*
The Perceptron Model
The Multilayer Feedforward Network
Filtering and Enhancing Images
What Needs Fixing?
An Image Needs Improvement
Low-Level Features Must Be Detected
Gray-Level Mapping
Histogram Equalization
Removal of Small Image Regions
Removal of Salt-and-Pepper Noise
Removal of Small Components
Image Smoothing
Median Filtering
Computing an Output Image from an Input Image
Detecting Edges Using Differencing Masks
Differencing 1D Signals
Difference Operators for 2D Images
Gaussian Filtering and LOG Edge Detection
Detecting Edges with the LOG Filter
On Human Edge Detection
Marr-Hildreth Theory
The Canny Edge Detector
Masks as Matched Filters*
The Vector Space of All Signals of n Samples
Using an Orthogonal Basis
Cauchy-Schwartz Inequality
The Vector Space of m [times] n Images
A Roberts Basis for 2 [times] 2 Neighborhoods
The Frei-Chen Basis for 3 [times] 3 Neighborhoods
Convolution and Cross Correlation*
Defining Operations via Masks
The Convolution Operation
Possible Parallel Implementations
Analysis of Spatial Frequency using Sinusoids*
A Fourier Basis
2D Picture Functions
Discrete Fourier Transform
Bandpass Filtering
Discussion of the Fourier Transform
The Convolution Theorem*
Summary and Discussion
Color and Shading
Some Physics of Color
Sensing Illuminated Objects
Additional Factors
Sensitivity of Receptors
The RGB Basis for Color
Other Color Bases
The CMY Subtractive Color System
HSI: Hue-Saturation-Intensity
YIQ and YUV for TV Signals
Using Color for Classification
Color Histograms
Color Segmentation
Radiation from One Light Source
Diffuse Reflection
Specular Reflection
Darkening with Distance
Phong Model of Shading*
Human Perception Using Shading
Related Topics*
Human Color Perception
Multispectral Images
Thematic Images
Texture, Texels, and Statistics
Texel-Based Texture Descriptions
Quantitative Texture Measures
Edge Density and Direction
Local Binary Partition
Co-occurrence Matrices and Features
Laws Texture Energy Measures
Autocorrelation and Power Spectrum
Texture Segmentation
Content-Based Image Retrieval
Image Database Examples
Image Database Queries
Image Distance Measures
Color Similarity Measures
Texture Similarity Measures
Shape Similarity Measures
Object Presence and Relational Similarity Measures
Database Organization
Standard Indexes
Spatial Indexing
Indexing for Content-Based Image Retrieval with Multiple Distance Measures
Motion From 2D Image Sequences
Motion Phenomena and Applications
Image Subtraction
Computing Motion Vectors
The Decathlete Game
Using Point Correspondences
MPEG Compression of Video
Computing Image Flow*
The Image Flow Equation*
Solving for Image Flow by Propagating Constraints*
Computing the Paths of Moving Points
Integrated Problem-Specific Tracking
Detecting Significant Changes in Video
Segmenting Video Sequences
Ignoring Certain Camera Effects
Storing Video Subsequences
Image Segmentation
Identifying Regions
Clustering Methods
Region Growing
Representing Regions
Labeled Images
Boundary Coding
Property Tables
Identifying Contours
Tracking Existing Region Boundaries
The Canny Edge Detector and Linker
Aggregating Consistent Neighboring Edgels into Curves
Hough Transform for Lines and Circular Arcs
Fitting Models to Segments
Identifying Higher-level Structure
Detecting Corners
Segmentation Using Motion Coherence
Boundaries in Space-Time
Aggregrating Motion Trajectories
Matching In 2D
Registration of 2D Data
Representation of Points
Affine Mapping Functions
A Best 2D Affine Transformation*
2D Object Recognition via Affine Mapping
2D Object Recognition via Relational Matching
Nonlinear Warping
Perceiving 3D From 2D Images
Intrinsic Images
Labeling of Line Drawings from Blocks World
3D Cues Available in 2D Images
Other Phenomena
Shape from X
Vanishing Points
Depth from Focus
Motion Phenomena
Boundaries and Virtual Lines
Alignments are Non-accidental
The Perspective Imaging Model
Depth Perception from Stereo
Establishing Correspondences
The Thin Lens Equation*
Concluding Discussion
3D Sensing and Object Pose Computation
General Stereo Configuration
3D Affine Transformations
Coordinate Frames
Arbitrary Rotation
Alignment via Transformation Calculus
Camera Model
Perspective Transformation Matrix
Orthographic and Weak Perspective Projections
Computing 3D Points Using Multiple Cameras
Best Affine Calibration Matrix
Calibration Jig
Defining the Least-Squares Problem
Discussion of the Affine Method
Using Structured Light
A Simple Pose Estimation Procedure
An Improved Camera Calibration Method*
Intrinsic Camera Parameters
Extrinsic Camera Parameters
Calibration Example
Pose Estimation*
Pose from 2D-3D Point Correspondences
Constrained Linear Optimization
Computing the Transformation Tr = [R, T]
Verification and Optimization of Pose
3D Object Reconstruction
Data Acquisition
Registration of Views
Surface Reconstruction
Computing Shape from Shading
Photometric Stereo
Integrating Spatial Constraints
Structure from Motion
3D Models and Matching
Survey of Common Representation Methods
3D Mesh Models
Surface-Edge-Vertex Models
Generalized-Cylinder Models
True 3D Models versus View-Class Models
Physics-Based and Deformable Models
Snakes: Active Contour Models
Balloon Models for 3D
Modeling Motion of the Human Heart
3D Object Recognition Paradigms
Matching Geometric Models via Alignment
Matching Relational Models
Matching Functional Models
Recognition by Appearance
Virtual Reality
Features of Virtual Reality Systems
Applications of VR
Augmented Reality (AR)
Virtual Reality Devices
Summary of Sensing Devices for VR
Rendering Simple 3D Models
Composing Real and Synthetic Imagery
HCI and Psychological Issues
Case Studies
Veggie Vision: A System for Checking Out Vegetables
Application Domain and Requirements
System Design
Identification Procedure
More Details on the Process
Identifying Humans via the Iris of an Eye
Requirements for Identification Systems
System Design
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