An interdisciplinary introduction to image processing : pixels, numbers, and programs / Steven L. Tanimoto.

Format:

Book

Author/Creator:

Tanimoto, S. (Steven)

Language:

English

Subjects (All):

Image processing.

Physical Description:

xxii, 521 p., [20] p. of plates : ill. (some col.)

Edition:

1st ed.

Place of Publication:

Cambridge, Mass. : MIT Press, c2012.

Language Note:

English

Summary:

Basic principles of image processing and programming explained without college-level mathematics.

Contents:

Intro

Contents

Preface

Acknowledgments

I IMAGES AND FORMULAS

1 Introduction

1.1 Sources of Images

1.2 What Is an Image?

1.3 What Is a Pixel?

1.4 What Is a Function?

1.5 What Is a Program?

1.6 Human Vision

1.7 How Does a Digital Camera Work?

1.8 What Can We Do with All These Pixels?

1.9 References

1.10 Exercises

2 Getting Started

2.1 Representation of Digital Images

2.2 Starting PixelMath

2.3 Examining Your Image

2.4 Put a Twinkle in Your Eye

2.5 A Quick Overview of Features

2.6 References

2.7 Exercises

3 Brightness and Contrast

3.1 Brightness

3.2 Contrast

3.3 Histograms

3.4 Thresholding

3.5 Quantization

3.6 Bits and Bytes

3.7 The Memory Required by an Image

3.8 References

3.9 Exercises

4 Controlling Color

4.1 The Phenomenon of Color

4.2 Controlling Color in PixelMath

4.3 Specific Colors and Palettes

4.4 Color Enhancements

4.5 Specifying Color Components in Formulas

4.6 Hue, Saturation, and Value

4.7 Indexed Color Formats, GIF and PNG

4.8 References

4.9 Exercises

5 Geometric Transformations

5.1 Overview

5.2 Scaling with One Window

5.3 Push versus Pull

5.4 Setting Up for Two-Window Operations

5.5 Widths and Heights

5.6 Reflections

5.7 90-Degree Rotations

5.8 Two Transformations in Succession

5.9 Modular Arithmetic on Coordinates

5.10 Resampling

5.11 Composition of Transformations

5.12 Invertibility and the Identity

5.13 Closure

5.14 Groups

5.15 Summary

6 Geometric Distortions

6.1 Perturbing Coordinates

6.2 Modulator Images

6.3 Invertibility Once Again

6.4 Polar Coordinates

6.5 Angles and Radii

6.6 Visualizing the Polar Coordinates

6.7 Transformations with Polar Coordinates

6.8 Fisheye Transformations

6.9 Ripples on a Pond.

6.10 Polar Pixellation Effects

6.11 About Anamorphic Images

6.12 Definition and Motivation

6.13 Obtaining a Cylindrical Mirror

6.14 Slant Anamorphosis

6.15 Cylinder Anamorphosis

6.16 Connections

6.17 Tuning Parameters

6.18 References

6.19 Exercises

7 Synthesizing Images

7.1 Rendering Points

7.2 Drawing Lines

7.3 Nonlinear Functions of the Form y = f(x)

7.4 Other Planar Figures

7.5 Functions of Form z = f(x,y)

7.6 Mountain Ranges and Distance Transforms

7.7 Making Curved Surfaces

7.8 Exercises

8 Stereograms

8.1 Introduction to Stereo

8.2 Traditional Stereo Pairs

8.3 Autostereograms

8.4 Making an Autostereogram: Basic Ideas

8.5 Challenges

8.6 Further Discussion

8.7 References

8.8 Exercises

9 Images Within Images

9.1 What Is Steganography?

9.2 Discovering Information by Stretching Contrast

9.3 Hiding Messages Using Key Values

9.4 Hiding Messages Using Bitwise Exclusive-OR

9.5 Hiding and Finding Messages Using Masks

9.6 Scrambling and Unscrambling

9.7 Watermarks

9.8 Photomosaics

9.9 Panorama Stitching

9.10 Fractals

9.11 References

9.12 Exercises

10 Filtering

10.1 Introduction

10.2 Filtering Out Noise

10.3 Median Filtering

10.4 High-Pass Filtering and Sharpening

10.5 Convolution

10.6 Systems of Filters

10.7 The Discrete Cosine Transform

10.8 Vectors and Vector Spaces

10.9 Bases for Vector Spaces

10.10 Image Vector Spaces

10.11 Bases of Complex Exponentials

10.12 Fourier Transforms

10.13 Two-Dimensional Fourier Transforms

10.14 Filtering with the Fourier Transform

10.15 References

10.16 Exercises

II IMAGES AND PROGRAMS

11 Introducing Python

11.1 Why Use Programming in PixelMath?

11.2 Introducing the Programming Interface

11.3 Editing Sequences of Commands.

11.4 How to Deal with Errors in the Commands

11.5 A Longer Example

11.6 PixelMath Commands

11.7 An Example: Twin Monas

11.8 References

12 Basics of Python

12.1 Overview

12.2 Interacting with Python

12.3 Numbers

12.4 Variables

12.5 Arithmetic Expressions

12.6 Strings

12.7 Lists

12.8 Built-in Functions

12.9 Modules and Importing

12.10 Comments in Code

12.11 Example

12.12 References

12.13 Exercises

13 Control Structures: Conditionals and Repetition

13.1 Overview

13.2 Conditionals

13.3 Boolean Values

13.4 Multiple Cases

13.5 Loops

13.6 Nested Loops

13.7 Looping to Enlarge an Image

13.8 Loops Using while

13.9 Setting up Multiple Windows

13.10 Building Convolution Formulas

13.11 Four-Level Nesting for Median Filtering

13.12 Iterative Transformations

13.13 Dissolves

13.14 Anatomy of a Loop

13.15 Progressive Scrambling

13.16 Handling Exceptions

13.17 Exercises

14 Data Structures

14.1 Overview

14.2 Strings

14.3 Lists

14.4 Tuples

14.5 Dictionaries

14.6 Classes

14.7 Trees

14.8 Graphs

14.9 References

14.10 Exercises

15 Creating Functions

15.1 Encapsulating Sequences of Operations

15.2 Defining Simple Python Functions

15.3 Functions with Parameters

15.4 Software Engineering with Functions

15.5 Returned Values versus Side Effects

15.6 Parameter List Options

15.7 Recursive Functions

15.8 Functional Programming Techniques

15.9 References

15.10 Exercises

16 Programming Techniques

16.1 Overview

16.2 Concepts and Paradigms

16.3 Imperative Programming

16.4 Object-Oriented Programming

16.5 Functional Programming

16.6 Callable Objects

16.7 A Three-Paradigm Presentation

16.8 Some Details About the Example

16.9 Scaffolding, Testing, and Debugging.

16.10 Considerations for Large Projects

16.11 References

16.12 Exercises

17 Image Analysis

17.1 Overview

17.2 Applications of Image Analysis

17.3 Boundary Finding

17.4 Contour and Line Finding

17.5 Connected Components

17.6 Morphology

17.7 Other Features

17.8 Exercises

18 Pattern Recognition

18.1 Overview

18.2 K-Nearest-Neighbor Classification

18.3 Perceptrons

18.4 The K-Means Clustering Method

18.5 References

18.6 Exercises

19 Computational Photography

19.1 Overview

19.2 High-Dynamic-Range Photography

19.3 Automatic Focus Methods

19.4 Multiple-Focus Photography

19.5 Ultra-High-Resolution Photography

19.6 Catadioptric Imaging

19.7 Separating Direct and Global Illumination

19.8 Image Inpainting

19.9 Other Developments

19.10 References

19.11 Exercises

20 Selected Applications

20.1 Overview

20.2 Morphing

20.3 Photomosaics with Many Images

20.4 An Image-Matching Game

20.5 References

20.6 Exercises

APPENDIXES

A Calculator Formulas

B Quick Reference-PixelMath Formulas

C Python Glossary

D Troubleshooting

E Graphical User Interfaces

Index.

Notes:

Title from title screen.

Digitized and made available by: Books24x7.com.

Includes bibliographical references and index.

ISBN:

0-262-30061-3

0-262-30139-3