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El. knyga: Nonlinear Image Processing

3.57/5 (7 ratings by Goodreads)
(University of Trieste, Italy), Edited by (University of California, Santa Barbara, U.S.A.)
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Signal and image processing have both recently experienced a massive influx of research into nonlinear methods and techniques. There are several reasons for this, two of the predominate include the fact that the human visual system contains non-linear aspects which need to be addressed in order to develop effective image processing algorithms, and that images generally do not satisfy the generally held hypothesis of Gaussianity and stationarity, and cannot, therefore, remove impulsive noise superimposed on an image without blurring its edges. This technical introductory text discusses several of the nonlinear options available for signal processing including homomorphic filters, mean filters, morphological filters, polynomial filters and fuzzy filters. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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

Recenzijos

"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."

--IEEE SIGNAL PROCESSING MAGAZINE, January 2001

Daugiau informacijos

* 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
Preface ix Analysis and Optimization of Weighted Order Statistic and Stack Filters 1(26) S. Peltonen P. Kuosmanen K. Egiazarian M. Gabbouj J. Astola Introduction 1(1) Median and Order Statistic Filters 1(1) Stack Filters 2(19) Image Processing Applications 21(1) Summary 22(5) Image Enhancement and Analysis with Weighted Medians 27(42) G. Arce J. Paredes Introduction 27(1) Weighted Median Smoothers and Filters 28(17) Image Denoising 45(4) Image Zooming 49(3) Image Sharpening 52(6) Optimal Frequency Selection WM Filtering 58(4) Edge Detection 62(3) Conclusion 65(4) Spatial--Rank Order Selection Filters 69(42) K. Barner R. Hardie Introduction 69(3) Selection Filters and Spatial-Rank Ordering 72(9) Spatial--Rank Order Selection Filters 81(13) Optimization 94(2) Applications 96(9) Future Directions 105(6) Signal-Dependent Rank-Ordered-Mean (SD-ROM) Filter 111(24) E. Abreu Introduction 111(1) Impulse Noise Model 112(1) Definitions 113(1) The SD-ROM Filter 114(2) Generalized SD-ROM Method 116(5) Experimental Results 121(5) Restoration of Images Corrupted by Streaks 126(5) Concluding Remarks 131(4) Nonlinear Mean Filters and Their Applications in Image Filtering and Edge Detection 135(32) C. Kotropoulos M. Pappas I. Pitas Introduction 135(1) Nonlinear Mean Filters 136(4) Signal-Dependent Noise Filtering by Nonlinear Means 140(1) Edge Detectors Based on Nonlinear Means 141(1) Grayscale Morphology Using ℒp Mean Filters 142(5) Ultrasonic Image Processing Using ℒ2 Mean Filters 147(10) Sorting Networks Using ℒp Mean Comparators 157(3) Edge Preserving Filtering by Combining Nonlinear Means and Order Statistics 160(3) Summary 163(4) Two-Dimensional Teager Filters 167(36) S. Thurnhofer Introduction 167(1) Discrete Volterra Series and Properties 167(4) Interpretation of Frequency Responses 171(1) The Teager Algorithm and One-Dimensional Extensions 172(5) Spectrum of the Output Signal 177(2) Mean-Weighted Highpass Filters 179(6) Least-Squares Design of Edge Extracting Filters 185(9) Summary 194(1) Appendix 195(8) Polynomial and Rational Operators for Image Processing and Analysis 203(22) G. Ramponi Introduction 203(1) Theoretical Survey of Polynomial and Rational Filters 204(4) Applications of Polynomial Filters 208(6) Applications of Rational Filters 214(7) Conclusions and Remaining Issues 221(4) Nonlinear Partial Differential Equations in Image Processing 225(24) G. Sapiro Introduction 225(3) Segmentation of Scalar and Multivalued Images 228(7) Nonlinear PDEs in General Manifolds: Harmonic Maps and Direction Diffusion 235(14) Region-Based Filtering of Images and Video Sequences: A Morphological Viewpoint 249(40) P. Salembier Introduction 249(2) Classical Filtering Approaches 251(3) Connected Operators 254(2) Connected Operators Based on Reconstruction Processes 256(8) Connected Operators Based on Region-Tree Pruning 264(19) Conclusions 283(6) Differential Morphology 289(42) P. Maragos Introduction 289(5) 2D Morphological Systems and Slope Transforms 294(6) PDEs for Morphological Image Analysis 300(8) Curve Evolution 308(2) Distance Transforms 310(8) Eikonal PDE and Distance Propagation 318(5) Conclusions 323(8) Coordinate Logic Filters: Theory and Applications in Image Analysis 331(24) B. Mertzios K. Tsirikolias Introduction 331(2) Coordinate Logic Operations on Digital Signals 333(4) Derivation of the Coordinate Logic Filters 337(2) Properties of Coordinate Logic Filters 339(1) Morphological Filtering Using Coordinate Logic Operations on Quantized Images 340(2) Image Analysis and Pattern Recognition Applications 342(10) Concluding Remarks 352(3) Nonlinear Filters Based on Fuzzy Models 355(20) F. Russo Introduction 355(1) Fuzzy Models 356(3) Fuzzy Weighted Mean (FWM) Filters 359(4) FIRE Filters 363(3) Evolutinary Neural Fuzzy Filters: A Case Study 366(6) Concluding Remarks and Future Trends 372(3) Digital Halftoning 375(28) D. Lau G. Arce Introduction 375(6) Halftone Statistics 381(4) Blue-Noise Dithering 385(5) Green-Noise Dithering 390(8) Conclusions 398(5) Intrinsic Dimensionality: Nonlinear Image Operators and Higher-Order Statistics 403(46) C. Zetzsche G. Krieger Introduction 403(3) Transdisciplinary Relevance of Intrinsic Dimensionality 406(7) i2D-Selective Nonlinear Operators 413(8) Frequency Design Methods for i2D Operators 421(11) i2D Operators and Higher-Order Statistics 432(5) Discussion 437(12) Index 449
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