K.N. Plataniotis and A.N. Venetsanopoulos
Color Image Processing and Applications
Engineering - Monograph (English)
Springer-Verlag, Berlin 2000
Preface
The perception of colour is of paramount importance to humans since they
routinely use colour features to sense the environment, recognize objects
and convey information. Colour image processing and analysis is concerned
with the manipulation of digital colour images on a computer utilizing
digital signal processing techniques. Like most advanced signal processing
techniques, it was, until recently, confined to academic institutions and
research laboratories that could afford the expensive image processing
hardware needed to handle the processing overhead required to process large
numbers of colour images. However, with the advent of powerful desktop
computers and the proliferation of image collection devices, such as
digital cameras and scanners, colour image processing techniques are now
within the grasp of the general public.
This book is aimed at researchers and practitioners that work in the area
of colour image processing. Its purpose is to fill an existing gap in
scientific literature by presenting the state of the art research in the
area. It is written at a level which can be easily understood by a
graduate student in an Electrical and Computer Engineering or Computer
Science program. Therefore, it can be used as a textbook that covers part
of a modern graduate course in digital image processing or multimedia
systems. It can also be used as a textbook for a graduate course on
digital signal processing since it contains algorithms, design criteria and
architectures for processing and analysis systems.
The book is structured into four parts. The first, Chapter 1, deals with
colour principles and is aimed at readers who have very little prior
knowledge of colour science. Readers interested in colour image processing
may read the second part of the book (Chapers 2-5). It covers the major,
although somewhat mature, fields of colour image processing. Colour image
processing is characterized by a large number of algorithms that are
specific solutions to specific problems, for example vector median filters
have been developed to remove impulsive noise from images. Some of them are
mathematical or content independant operations that are applied to each and
every pixel, such as morphological operators. Others are algorithmic in
nature, in the sense that a recursive strategy may be necessary to find
edge pixels in an image.
The third part of the book, Chapters 6-7, deals with colour image analysis
and coding techniques. The ultimate goal of colour image analysis is to
enhance human-computer interaction. Recent applications of image analysis
includes compression of colour images either for transmission across the
internetwork or coding of video images for video conferencing. Finally,
the fourth part (Chapter 8) covers emerging applications of colour image
processing. Colour is useful for accessing multimedia databases. Local
colour information, for example in the form of colour histograms, can be
used to index and retrieve images from the database. Colour features can
also be used to identify objects of interest, such as human faces and hand
areas, for applications ranging from video conferencing, to perceptual
interfaces and virtual environments.
Acknowledgement
We acknowledge a number of individuals who have contributed in different
ways to the preparation of this book. In particular, we wish to extend our
appreciation to Prof. M. Zervakis for contributing the image restoration
section, and to Dr. N. Herodotou for his informative inputs and valuable
suggestions in the emerging applications chapter. Three graduate students
of ours also merit special thanks. Shu Yu Zhu for her input and high
quality figures included in the colour edge detection chapter, Ido
Rabinovitch for his contribution to the colour image coding section and
Nicolaos Ikonomakis for his valuable contribution in the colour
segmentation chapter. We also thank Nicolaos for reviewing the chapters of
the book and helping with the Latex formating of the manuscript. We are
also grateful to Terri Vlassopoulos for proofreading the manuscript, and
Frank Holzwarth of Springer Verlag for his help during the preparation of
the book. Finally, we are indebted to Peter Androutsos who helped us
tremendously on the development of the companion software.
Contents
1. Color Spaces
1.1 Basics of Color Vision
1.2 The CIE Chromaticity-based Models
1.3 The CIE-RGB Color Model
1.4 Gamma Correction
1.5 Linear and Non-linear RGB Color Spaces
1.5.1 Linear RGB Color Space
1.5.2 Non-linear RGB Color Space
1.6 Color Spaces Linearly Related to the RGB
1.7 The YIQ Color Space
1.8 The HSI Family of Color Models
1.9 Perceptually Uniform Color Spaces
1.9.1 The CIE L*u*v* Color Space
1.9.2 The CIE L*a*b* Color Space
1.9.3 Cylindircal L*u*v* and L*a*b* Color Space
1.9.4 Applications of L*u*v* and L*a*b* spaces
1.10 The Munsell Color Space
1.11 The Opponent Color Space
1.12 New Trends
1.13 Color Images
1.14 Summary
2. Color Image Filtering
2.1 Introduction
2.2 Color Noise
2.3 Modeling Sensor Noise
2.4 Modeling Transmission Noise
2.5 Multivariate Data Ordering Schemes
2.5.1 Marginal Ordering
2.5.2 Conditional Ordering
2.5.3 Partial Ordering
2.5.4 Reduced Ordering
2.6 A Practical Example
2.7 Vector Ordering
2.8 The Distance Measures
2.9 The Similarity Measures
2.10 Filters Based on Marginal Ordering
2.11 Filters Based on Reduced Ordering
2.12 Filters Based on Vector Ordering
2.13 Directional Based Filters
2.14 Computational Complexity
2.15 Conclusion
3. Adaptive Image Filters
3.1 Introduction
3.2 The Adaptive Fuzzy System
3.2.1 Determining the Parameters
3.2.2 The Membership Function
3.2.3 The Generalized Membership Function
3.2.4 Members of the Adaptive Fuzzy Filter Family
3.2.5 A Combined Fuzzy Directional and Fuzzy Median Filter
3.2.6 Comments
3.2.7 Application to 1-D Signals
3.3 The Bayesian Parametric Approach
3.4 The Non-parametric Approach
3.5 Adaptive Morphological Filters
3.5.1 Introduction
3.5.2 Computation of the NOP and the NCP
3.5.3 Computational Complexity and Fast Algorithms
3.6 Simulation Studies
3.7 Conclusions
4. Color Edge Detection
4.1 Introduction
4.2 Overview of Color Edge Detection Methodology
4.2.1 Techniques Extended From Monochrome Edge Detection
4.2.2 Vector Space Approaches
4.3 Vector Order Statistic Edge Operators
4.4 Difference Vector Operators
4.5 Evaluation Procedures and Results
4.5.1 Probabilistic Evaluation
4.5.2 Noise Performance
4.5.3 Subjective Evaluation
4.6 Conclusion
5. Color Image Enhancement and Restoration
5.1 Introduction
5.2 Histogram Equalization
5.3 Color Image Restoration
5.4 Restoration Algorithms
5.5 Algorithm Formulation
5.5.1 Definitions
5.5.2 Direct Algorithms
5.5.3 Robust Algorithms
5.6 Conclusions
6. Color Image Segmentation
6.1 Introduction
6.2 Pixel-based Techniques
6.2.1 Histogram Thresholding
6.2.2 Clustering
6.3 Region-based Techniques
6.3.1 Region Growing
6.3.2 Split and Merge
6.4 Edge-based Techniques
6.5 Model-based Techniques
6.5.1 The Maximum A-posteriori Method
6.5.2 The Adaptive MAP Method
6.6 Physics-based Techniques
6.7 Hybrid Techniques
6.8 Application
6.8.1 Pixel Classification
6.8.2 Seed Determination
6.8.3 Region Growing
6.8.4 Region Merging
6.8.5 Results
6.9 Conclusion
7. Color Image Compression
7.1 Introduction
7.2 Image Compression Comparison Technology
7.3 Image Representation for Compression Applications
7.4 Lossless Waveform-based Image Compression Techniques
7.4.1 Entropy Coding
7.4.2 Lossless Compression Using Spatial Redundancy
7.5 Lossy Waveform-based Image Compression Techniques
7.5.1 Spatial Domain Methodologies
7.5.2 Transform Domain Methodologies
7.6 Second Generation Image Compression Techniques
7.7 Perceptually Motivated Compression Techniques
7.7.1 Modeling the Human Visual System
7.7.2 Perceptually Motivated DCT Image Coding
7.7.3 Perceptually Motivated Wavelet-based Coding
7.7.4 Perceptually Motivated Region-based Coding
7.8 Color Video Compression
7.9 Conclusion
8. Emerging Applications
8.1 Input Analysis Using Color Information
8.2 Shape and Color Analysis
8.2.1 Fuzzy Membership Functions
8.2.2 Aggregation Operations
8.3 Experimental Results
8.4 Conclusions
A. Companion Image Processing Software
A.1 Image Filtering
A.2 Image Analysis
A.3 Image Transforms
A.4 Noise Generation
Index