Résumé
The first image processing "cookbook" for
Java!
Image Processing in Java combines the most complete set of real-world Java-based image processing examples ever assembled in one book with a sound theoretical foundation for image processing. An image processing practicum that delivers thoroughly-tested, platform-independent algorithms you can use to build Java-based software for virtually any product or research development project.
Coverage includes:
On the CD-ROM :
The accompanying CD-ROM is PACKED with data and has been tested on Windows, MacOS and UNIX. It contains:
Owners of this book may obtain free access to the author, as well as to the source code for all the examples by going to http://lyon.bridgeport.edu
Table of contents
(NOTE: Most chapters contain a Class Summary, Class Usage and Class
Implementation.)
Image Processing in Java combines the most complete set of real-world Java-based image processing examples ever assembled in one book with a sound theoretical foundation for image processing. An image processing practicum that delivers thoroughly-tested, platform-independent algorithms you can use to build Java-based software for virtually any product or research development project.
Coverage includes:
- Restoration, compression, segmentation, transformation, representation and warping
- Displaying and filtering images in Java
- Homogeneous point processing functions and classes
- Image streams, file readers and writers
- Direct convolution, edge detection, morphological filtering, boundary processing, and more
- Designing CODECs and custom image formats
- Wavelets in Java
- Multi-resolution image processing
On the CD-ROM :
The accompanying CD-ROM is PACKED with data and has been tested on Windows, MacOS and UNIX. It contains:
- original source code not seen elsewhere (over 117 classes)
- a complete image processing application
- two graduate-level Java courses taught by the author
- a large image and image sequence database.
Owners of this book may obtain free access to the author, as well as to the source code for all the examples by going to http://lyon.bridgeport.edu
Table of contents
(NOTE: Most chapters contain a Class Summary, Class Usage and Class
Implementation.)
List of Figures and
Tables.
Acknowledgment.
Foreword.
Preface.
Why Should I Care About Java?
Who Should Read This Book?
Why Not Use the Advanced Imaging Package?
This Is Not Just a Textbook.
What Chapters Do I Need to Read?
How Can I Use the Web to Teach and Learn More Image Processing?
1. Introduction.
What is This Book About?
What is Image Processing? What is Java? Why Do Image Processing in
Java?
Who Should Read This Book? What Will You Learn?
2. Event Processing and the AWT.
The Frame Class. Interaction.
The EventTester. BooLog. ExpandoLog. Class Summary. Class Usage.
Summary. Suggested Projects.
3. Displaying and Filtering Images
Image Representation. Scalar Numeric Data Types. Arrays in Java.
Drawing in Java. The ImageFrame Class. The FilterFrame. The GrabFrame Class.
SnellWlx Class and Test Patterns.
4. Homogeneous Point Processing.
The Histogram Class. Homogeneous Point Processing Functions.
Using the Pow Function. Using Linear Transforms. The Uniform Non-
Adaptive Histogram Equalization. Maple and Exponential Non Adaptive Histogram
Equalization. Adaptive Histogram Equalization.
The TransformTable Class. The NegateFrame Class. Summary.
5. Digital Image Processing Fundamentals.
The Human Visual System. Overview of Image Processing.
Digitizing a Signal. Image Digitization. Image Display.
6. Input Streams.
Getting a FileName. File Input. The Stream Ubyte Class. The
StreamSniffer Class.
7. Image File Readers and Writers.
Getting a Filename and Writing a PPM File. Writing a Program That
Writes a Program. Writing Lossless 24-Bit Color Image Files. Reading GZIP
Compressed PPM Files. The SaveFrame Class. The OpenFrame Class. The Timer
Class.
Class Example: Loop Interchange.
The ProgressFrame. The WriteGif. The WritePPM Class.
The PPM. Class Example.
Summary. Projects.
8. Direct Convolution.
The Mathematical Basis of Convolution. The Cyclic Convolution,
Correctness and Speed. Generating Convolution Kernels - The Mat Class.
Implementing Direct Convolution. Summary.
9. Spatial Filters
Blurring. Median Filtering. High-Pass. The SpatialFilterFrame.
Summary. Suggested Projects.
10. Convolution-based Edge Detection.
Laplace Filter. Roberts. Sobel and the Double Templates. Comparing
Edge Detectors. Summary. Projects. EdgeFrame Class.
11. Morphological Filtering.
Set Theory. Erosion and Dilation. Opening and Closing. Outlining.
Thinning and Skeletonization. Summary. Projects. The MorphFrame.
12. Boundary Processing.
Hough Transform. Simple Edge Tracing. Edge Detection. Summary.
Projects. The BoundaryFrame. The MartelliFrame.
13. Chromatic Light.
Introduction to Color. Tristimulus.
RGB and XYZ for Illuminant D65. Sub-Sampling. YIQ and NTSC Encoders.
Linear Cut Color Reduction. The Median Cut Algorithm. Summary.
Projects. The ColorHash Class. The ColorFrame.
14. Warping.
Translation. Scaling. Rotation. Shear. The AffineFrame. Applying the
Transforms to an Image. Inferring a 3 Point Affine Mapping. Inferring a 4
Point Bilinear Map. Effects. Projects.
15. Unitary Transforms.
Introduction. The Discrete Fourier Transform. The Wavelet Transform.
The Haar Basis. Implementing the Two-Dimensional Haar Lifting. Writing a
Wavelet Encoded File. Summary. Projects.
Appendix A. Book Resources On-line.
Appendix B. The Kahindu Interface.
B.1. Icon Design.
B.2. Icon Design By Drawing.
B.3. Icon Design By Typing.
B.4. Saving the Icon as Java.
Appendix C. The Structure of Kahindu.
Appendix D. Index of Fields and Methods.
Literature Cited.
Index.
Colophon.
List of Figures and Tables.
Acknowledgment.
Foreword.
Preface.
Why Should I Care About Java?
Who Should Read This Book?
Why Not Use the Advanced Imaging Package?
This Is Not Just a Textbook.
What Chapters Do I Need to Read?
How Can I Use the Web to Teach and Learn More Image Processing?
1. Introduction.
What is This Book About?
What is Image Processing? What is Java? Why Do Image Processing in
Java?
Who Should Read This Book? What Will You Learn?
2. Event Processing and the AWT.
The Frame Class. Interaction.
The EventTester. BooLog. ExpandoLog. Class Summary. Class Usage.
Summary. Suggested Projects.
3. Displaying and Filtering Images
Image Representation. Scalar Numeric Data Types. Arrays in Java.
Drawing in Java. The ImageFrame Class. The FilterFrame. The GrabFrame Class.
SnellWlx Class and Test Patterns.
4. Homogeneous Point Processing.
The Histogram Class. Homogeneous Point Processing Functions.
Using the Pow Function. Using Linear Transforms. The Uniform Non-
Adaptive Histogram Equalization. Maple and Exponential Non Adaptive Histogram
Equalization. Adaptive Histogram Equalization.
The TransformTable Class. The NegateFrame Class. Summary.
5. Digital Image Processing Fundamentals.
The Human Visual System. Overview of Image Processing.
Digitizing a Signal. Image Digitization. Image Display.
6. Input Streams.
Getting a FileName. File Input. The Stream Ubyte Class. The
StreamSniffer Class.
7. Image File Readers and Writers.
Getting a Filename and Writing a PPM File. Writing a Program That
Writes a Program. Writing Lossless 24-Bit Color Image Files. Reading GZIP
Compressed PPM Files. The SaveFrame Class. The OpenFrame Class. The Timer
Class.
Class Example: Loop Interchange.
The ProgressFrame. The WriteGif. The WritePPM Class.
The PPM. Class Example.
Summary. Projects.
8. Direct Convolution.
The Mathematical Basis of Convolution. The Cyclic Convolution,
Correctness and Speed. Generating Convolution Kernels - The Mat Class.
Implementing Direct Convolution. Summary.
9. Spatial Filters
Blurring. Median Filtering. High-Pass. The SpatialFilterFrame.
Summary. Suggested Projects.
10. Convolution-based Edge Detection.
Laplace Filter. Roberts. Sobel and the Double Templates. Comparing
Edge Detectors. Summary. Projects. EdgeFrame Class.
11. Morphological Filtering.
Set Theory. Erosion and Dilation. Opening and Closing. Outlining.
Thinning and Skeletonization. Summary. Projects. The MorphFrame.
12. Boundary Processing.
Hough Transform. Simple Edge Tracing. Edge Detection. Summary.
Projects. The BoundaryFrame. The MartelliFrame.
13. Chromatic Light.
Introduction to Color. Tristimulus.
RGB and XYZ for Illuminant D65. Sub-Sampling. YIQ and NTSC Encoders.
Linear Cut Color Reduction. The Median Cut Algorithm. Summary.
Projects. The ColorHash Class. The ColorFrame.
14. Warping.
Translation. Scaling. Rotation. Shear. The AffineFrame. Applying the
Transforms to an Image. Inferring a 3 Point Affine Mapping. Inferring a 4
Point Bilinear Map. Effects. Projects.
15. Unitary Transforms.
Introduction. The Discrete Fourier Transform. The Wavelet Transform.
The Haar Basis. Implementing the Two-Dimensional Haar Lifting. Writing a
Wavelet Encoded File. Summary. Projects.
Appendix A. Book Resources On-line.
Appendix B. The Kahindu Interface.
B.1. Icon Design.
B.2. Icon Design By Drawing.
B.3. Icon Design By Typing.
B.4. Saving the Icon as Java.
Appendix C. The Structure of Kahindu.
Appendix D. Index of Fields and Methods.
Literature Cited.
Index.
Colophon.
List of Figures and Tables.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Prentice Hall |
| Auteur(s) | Douglas A.Lyon |
| Parution | 10/03/1999 |
| Nb. de pages | 450 |
| EAN13 | 9780139745775 |
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