Digital Image Processing: PIKS Scientific Inside

A newly updated and revised edition of the classic introduction to digital image processing
The Fourth Edition of Digital Image Processing provides a complete introduction to the field and includes new information that updates the state of the art. The text offers coverage of new topics and includes interactive computer display imaging examples and computer programming exercises that illustrate the theoretical content of the book. These exercises can be implemented using the Programmer′s Imaging Kernel System (PIKS) application program interface included on the accompanying CD.
Suitable as a textbook for students or as a reference for practitioners, this new edition provides a comprehensive treatment of these vital topics:Characterization of continuous imagesImage sampling and quantization techniquesTwo–dimensional signal processing techniquesImage enhancement and restoration techniquesImage analysis techniquesSoftware implementation of image processing applications
In addition, the bundled CD includes:A Solaris operating system executable version of the PIKS Scientific APIA Windows operating system executable version of PIKS ScientificA Windows executable version of PIKSTool, a graphical user interface method of executing many of the PIKS Scientic operators without program compilationA PDF file format version of the PIKS Scientific C programmer′s reference manualC program source demonstration programsA digital image database of most of the source images used in the book plus many others widely used in the literature

Spis treści:
Preface.
Acknowledgments.
PART 1 CONTINUOUS IMAGE CHARACTERIZATION.
1 Continuous Image Mathematical Characterization.
1.1 Image Representation.
1.2 Two–Dimensional Systems.
1.3 Two–Dimensional Fourier Transform.
1.4 Image Stochastic Characterizat ion.
2 Psychophysical Vision Properties.
2.1 Light Perception.
2.2 Eye Physiology.
2.3 Visual Phenomena.
2.4 Monochrome Vision Model.
2.5 Color Vision Model.
3 Photometry and Colorimetry.
3.1 Photometry.
3.2 Color Matching.
3.3 Colorimetry Concepts.
3.4 Tristimulus Value Transformation.
3.5 Color Spaces.
PART 2 DIGITAL IMAGE CHARACTERIZATION.
4 Image Sampling and Reconstruction.
4.1 Image Sampling and Reconstruction Concepts.
4.2 Monochrome Image Sampling Systems.
4.3 Monochrome Image Reconstruction Systems.
4.4 Color Image Sampling Systems.
5 Image Quantization.
5.1 Scalar Quantization.
5.2 Processing Quantized Variables.
5.3 Monochrome and Color Image Quantization.
PART 3 DISCRETE TWO–DIMENSIONAL PROCESSING.
6 Discrete Image Mathematical Characterization.
6.1 Vector–Space Image Representation.
6.2 Generalized Two–Dimensional Linear Operator.
6.3 Image Statistical Characterization.
6.4 Image Probability Density Models.
6.5 Linear Operator Statistical Representation.
7 Superposition and Convolution.
7.1 Finite–Area Superposition and Convolution.
7.2 Sampled Image Superposition and Convolution.
7.3 Circulant Superposition and Convolution.
7.4 Superposition and Convolution Operator Relationships.
8 Unitary Transforms.
8.1 General Unitary Transforms.
8.2 Fourier Transform.
8.3 Cosine, Sine and Hartley Transforms.
8.4 Hadamard, Haar and Daubechies Transforms.
8.5 Karhunen–Loeve Transform.
9 Linear Processing Techniques.
9.1 Transform Domain Processing.
9.2 Transform Domain Superposition.
9.3 Fast Fourier Transform Convolution.
9.4 Fourier Transform Filtering.
9.5 Small Generating Kernel Convolution.
PART 4 IMAGE IMPROVEMENT.
10 Image Enhancement.
10.1 Contrast Manipulation.
10.2 Histogram Modification.
10.3 Noise Cleaning.
10.4 Edge Crispening.
10.5 Color Image Enhancement.
10.6 Multispectral Image Enhancement.
11 Image Restoration Models.
11.1 General Image Restoration Models.
11.2 Optical Systems Models.
11.3 Photographic Process Models.
11.4 Discrete Image Restoration Models.
12 Image Restoration Techniques.
12.1 Sensor and Display Point Nonlinearity Correction.
12.2 Continuous Image Spatial Filtering Restoration.
12.3 Pseudoinverse Spatial Image Restoration.
12.4 SVD Pseudoinverse Spatial Image Restoration.
12.5 Statistical Estimation Spatial Image Restoration.
12.6 Constrained Image Restoration.
12.7 Blind Image Restoration.
12.8 Multi–Plane Image Restoration.
13 Geometrical Image Modification.
13.1 Basic Geometrical Methods.
13.2 Spatial Warping.
13.3 Perspective Transformation.
13.4 Camera Imaging Model.
13.5 Geometrical Image Resampling.
PART 5 IMAGE ANALYSIS.

14 Morphological Image Processing.
14.1 Binary Image Connectivity.
14.2 Binary Image Hit or Miss Transformations.
14.3 Binary Image Shrinking, Thinning, Skeletonizing and Thickening.
14.4 Binary Image Generalized Dilation and Erosion.
14.5 Binary Image Close and Open Operations.
14.6 Gray Scale Image Morphological Operations.
15 Edge Detection.
15.1 Edge, Line and Spot Models.
15.2 First–Order Derivative Edge Detection.
15.3 Second–Order Derivative Edge Detection.
15.4 Edge–Fitting Edge Detection.
15.5 Luminance Edge Detector Performance.
15.6 Color Edge Detection.
15.7 Line and Spot Detection.
16 Image Feature Extraction.
16.1 Image Feature Evaluation.
16.2 Amplitude Features.
16.3 Transform Coefficient Features.
16.4 Texture Definition.
16.5 Visual Texture Discrimination.
16.6 Texture Features.
17 Image Segmentation.
17.1 Amplitude Segmentation.
17.2 Clustering Segmentation.
17.3 Region Segmentation.
17.4 Boundary Segmentation.
17.5 Texture Segmentation.
17.6 Segment Labeling.
18 Shape Analysis.
18.1 Topological Attributes.
18.2 Distance, Perimeter and Area Measurements.
18.3 Spatial Moments.
18.4 Shape Orientation Descriptors.
18.5 Fourier Descriptors.
18.6 Thinning and Skeletonizing.
19 Image Detection and Registration.

19.1 Template Matching.
19.2 Matched Filtering of Continuous Images.
19.3 Matched Filtering of Discrete Images.
19.4 Image Registration.
PART 6 IMAGE PROCESSING SOFTWARE.
20 PIKS Image Processing Software.
20.1 PIKS Functional Overview.
20.2 PIKS Scientific Overview.
21 PIKS Image Processing Programming Exercises.
21.1 Program Generation Exercises.
21.2 Image Manipulation Exercises.
21.3 Color Space Exercises.
21.4 Region–of–Interest Exercises.
21.5 Image Measurement Exercises.
21.6 Quantization Exercises.
21.7 Convolution Exercises.
21.8 Unitary Transform Exercises.
21.9 Linear Processing Exercises.
21.10 Image Enhancement Exercises.
21.11 Image Restoration Models Exercises.
21.12 Image Restoration Exercises.
21.13 Geometrical Image Modification Exercises.
21.14 Morphological Image Processing Exercises.
21.15 Edge Detection Exercises.
21.16 Image Feature Extraction Exercises.
21.17 Image Segmentation Exercises.
21.18 Shape Analysis Exercises.
21.19 Image Detection and Registration Exercises.
Appendix 1 Vector–Space Algebra Concepts.
Appendix 2 Color Coordinate Conversion.
Appendix 3 Image Error Measures.
Appendix 4 PIKS Compact Disk.
Bibliography.
Index.

Nota biograficzna:
WILLIAM K. PRATT, PhD, has worked in imaging technology at the University of Southern California, Vicom Systems, Sun Microsystems, and, more recently, at PixelSoft. He is the author of numer