"Analysis and Optimization of Weighted Order Statistics and Stack Filters"
S. Siren, P. Kuosmainen, K. Egiazarian, M. Gabbouj and J. Astola 1.1 Introduction 1.2 Median and Order Statistic Filters 1.3 Stack Filters 1.4 Image Processing Applications 1.5 Summary
"Image Enhancement and Analysis with Weighted Medians"G. Arce and J. L. Paredes
2.1 Introduction 2.2 Weighted Median Smoothers and Filters 2.3 Image Denoising 2.4 Image Zooming 2.5 Image Sharpening 2.6 Optimal Frequency Selection WM Filtering 2.7 Edge Detection 2.8 Conclusion
"Spatial-Rank Order Selection Filters"R. Hardie and K. Barner
3.1 Introduction 3.2 Selection Filters and Spatial-Rank Ordering 3.3 Spatial-Rank Order Selection Filters 3.4 Optimization 3.5 Applications 3.6 Future Directions
"Signal-Dependent Rank-Ordered Mean (SD-ROM) Filter" E. Abreu 4.1 Introduction 4.2 Impulse Noise Model 4.3 Definitions 4.4 The SD-ROM Filter 4.5 Generalized SD-ROM Method 4.6 Experimental Results 4.7 Restoration of Images Corrupted by Streaks 4.8 Conclusing Remarks
"Nonlinear Mean Filters and Their Applications in Image Filtering and Edge Detection" M. Pappas and I. Pitas 5.1 Introduction 5.2 Nonlinear Mean Filters 5.3 Signal-Dependent Filtering by Nonlinear Means 5.4 Edge Detectors Based on Nonlinear Means 5.5 Grayscale Morphology Using Lp Mean Filters 5.6 Ultrasonic Umage Processing Using L2 Men Filters 5.7 Sorting Networks Using Lp Mean Comparators 5.8 Edge Preserving Filtering by Combining Nonlinear Means and Order Statistics
"Two-Dimensional Teager Filters" S. Thurnhofer 6.1 Introduction 6.2 Discrete Volterra Series and Properties 6.3 Interpretation of Frequency Responses 6.4 The Teager Algorithm and One-Dimensional Extensions 6.5 Spectrum of the Output Signal 6.6 Mean-Weighted Highpass Filters 6.7 Least-Squares Design of Edge Extracting Filters 6.8 Summary 6.9 Appendix
"Polynomial and Rational Operators for Image Processing and Analysis" G. Ramponi 7.1 Introduction 7.2 Theoretical Survey of Polynomial and Rational Filters 7.3 Applications of Polynomial Filters 7.4 Applications of Rational Filters 7.5 Conclusions and Remaining Issues
"Nonlinear Partial Differential Equations in Image Processing" G. Sapiro 8.1 Introduction 8.2 Segmentation of Scalar and Multivalued Images 8.3 Nonlinear PDEs in General Manifolds: Harmonic Maps and Direction Diffusion
"Region-Based Filtering of Images and Video Sequences: a Morphological Viewpoint" P. Salembier 9.1 Introduction 9.2 Classical Filtering Approaches 9.3 Connected Operators 9.4 Connected Operators Based on Reconstruction Processes 9.5 Connected Operators Based on Region-Tree Pruning 9.6 Conclusions
"Differential Morphology" P. Maragos 10.1 Introduction 10.2 2D Morphological Systems and Slope Transforms 10.3 PDEs for Morphological Image Analysis 10.4 Curve Evolution 10.5 Distance Tranforms 10.6 Eikonal PDE and Distance Propagation
"Coordinate Logic Filters" B.G. Mertzios and K. Tsirikolias 11.1 Introduction 11.2 Coordinate Logic Operations on Digital Signals 11.3 Derivation of the Coordinate Logic Filters 11.4 Properties of Coordinate Logic Filters 11.5 Morphological Filtering Using Coordinate Logic Operations on Quantized Images 11.6 Image Analysis and Pattern Recognition Applications 11.7 Concluding Remarks
"Nonlinear Filters Based on Fuzzy Models" F. Russo 12.1 Introduction 12.2 Fuzzy Models 12.3 Fuzzy Weighted Mean (FWM) Filters 12.4 FIRE Filters 12.5 Evolutionary Neural Fuzzy Filters: A Case Study 12.6 Concluding Remarks Future Trends
"Digital Halftoning" G. Arce and D. Lau 13.1 Introduction 13.2 Halftone Statistics 13.3 Blue-Noise Dithering 13.4 Green-Noise Dithering 13.5 Conclusions
"Intrinsic Dimensionality: Nonlinear Image Operators and Higher Order Statistics" C. Zetzsche and G. Krieg 14.1 Introduction 14.2 Transdisciplinary Relevance of Intrinsic Dimensionality 14.3 i2D-Selective Nonlinear Operators 14.4 Frequence Design Methods for i2D Operators 14.5 i2D Operators and Higher-Order Statistics 14.6 Discussions
This state-of-the-art book deals with the most important aspects of non-linear imaging challenges. The need for engineering and mathematical methods is essential for defining non-linear effects involved in such areas as computer vision, optical imaging, computer pattern recognition, and industrial automation challenges.
- Presents the latest developments in a variety of filter design techniques and algorithms
- Contains essential information for development of Human Vision Systems (HVS)
- Provides foundations for digital imaging and image capture technology
Electrical and computer engineers.
- No. of pages:
- © Academic Press 2001
- 5th September 2000
- Academic Press
- eBook ISBN:
"The book considers the following filter families, with varying emphasis, according to popularity and impact in image processing tasks:
- honomorphic filters, relying on a generalized superposition principle
- nonlinear mean filters, using nonlinear definitions of means
- morphological filters, based on geometrical rather than analytical properties
- order statistics filters, based on ordering properties of the input samples
- polynomial filters, using polynomial expressions in the input and output samples
- fuzzy filters, applying fuzzy reasoning to model the uncertainty typical of some image processing issues
- nonlinear operations modeled in terms of nonlinear partial differential equations."
University of California, Santa Barbara, U.S.A.
University of Trieste, Italy