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 Cham, Switzerland : Springer, [2018]
 Description
 Book — xvii, 228 pages : illustrations (chiefly color) ; 27 cm
 Summary

 Preface.
 1 Introduction.
 2 Optimizing Image Quality.
 3 Computational Color Imaging.
 4 Optimization Methods for SAR.
 5 Computational Spectral Ultrafast Imaging.
 6 Discriminative Sparse Representation.
 7 Sparsitybased Nonlocal Image Restoration.
 8 Sparsity Constrained Estimation.
 9 Optimization Problems Associated with Manifolds.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9783319616087 20180604
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TA1637 .H35 2018  Unknown 
 Totsky, Alexander V., 1952
 Berlin ; Boston : Walter de Gruyter GmbH & Co. KG, [2015]
 Description
 Book — x, 199 pages : illustrations ; 25 cm
 Summary

 General properties of bispectrumbased digital signal processing
 Unknown noisy signal shape estimation by bispectrumfiltering techniques
 Bispectrumbased digital image reconstruction using tapering predistortion
 Signal detection by using thirdorder test statistics in communications and radar applications.
(source: Nielsen Book Data) 9783110374568 20160618
 Online
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TK5102.9 .T738 2015  Unknown 
 Tanimoto, S. (Steven)
 Cambridge, Mass. : MIT Press, c2012.
 Description
 Book — xxii, 521 p., [20] p. of plates : ill. (some col.) ; 24 cm.
 Summary

This book explores image processing from several perspectives: the creative, the theoretical (mainly mathematical), and the programmatical. It explains the basic principles of image processing, drawing on key concepts and techniques from mathematics, psychology of perception, computer science, and art, and introduces computer programming as a way to get more control over image processing operations. It does so without requiring collegelevel mathematics or prior programming experience. The content is supported by PixelMath, a freely available software program that helps the reader understand images as both visual and mathematical objects. The first part of the book covers such topics as digital image representation, sampling, brightness and contrast, color models, geometric transformations, synthesizing images, stereograms, photomosaics, and fractals. The second part of the book introduces computer programming using an opensource version of the easytolearn Python language. It covers the basics of image analysis and pattern recognition, including edge detection, convolution, thresholding, contour representation, and Knearestneighbor classification. A chapter on computational photography explores such subjects as highdynamicrange imaging, autofocusing, and methods for automatically inpainting to fill gaps or remove unwanted objects in a scene. Applications described include the design and implementation of an imagebased game. The PixelMath software provides a "transparent" view of digital images by allowing the user to view the RGB values of pixels by zooming in on an image. PixelMath provides three interfaces: the pixel calculator; the formula page, an advanced extension of the calculator; and the Python window.
(source: Nielsen Book Data) 9780262017169 20160609
 Online
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TA1637 .T37 2012  Unknown 
4. Stochastic geometry for image analysis [2012]
 London ; Hoboken, NJ : ISTE/Wiley, 2012.
 Description
 Book — x, 345 p. : ill. ; 24 cm.
 Summary

 Chapter 1. Introduction
 1 X. DESCOMBES
 Chapter 2. Marked Point Processes for Object Detection
 11 X. DESCOMBES 2.1. Principal definitions
 11 2.2. Density of a point process
 15 2.3. Marked point processes
 21 2.4. Point processes and image analysis
 22
 Chapter 3. Random Sets for Texture Analysis
 29 C. LANTUEJOUL and M. SCHMITT 3.1. Introduction
 29 3.2. Random sets
 33 3.3. Some geostatistical aspects
 42 3.4. Some morphological aspects
 51 3.5. Appendix: demonstration of Miles formulae for the Boolean model
 61
 Chapter 4. Simulation and Optimization
 65 F. LAFARGE, X. DESCOMBES, E. ZHIZHINA and R. MINLOS 4.1. Discrete simulations: Markov chain Monte Carlo algorithms
 66 4.2. Continuous simulations
 91 4.3. Mixed simulations
 105 4.4. Simulated annealing
 106
 Chapter 5. Parametric Inference for Marked Point Processes in Image Analysis
 113 R. STOICA, F. CHATELAIN and M. SIGELLE 5.1. Introduction
 113 5.2. First question: what and where are the objects in the image?
 117 5.3. Second question: what are the parameters of the point process that models the objects observed in the image?
 129 5.4. Conclusion and perspectives
 158 5.5. Acknowledgments
 159
 Chapter 6. How to Set Up a Point Process?
 161 X. DESCOMBES 6.1. From disks to polygons, via a discussion of segments
 162 6.2. From no overlap to alignment
 167 6.3. From the likelihood to a hypothesis test
 172 6.4. From Metropolis Hastings to multiple births and deaths
 176
 Chapter 7. Population Counting
 179 X. DESCOMBES 7.1. Detection of Virchow Robin spaces
 180 7.2. Evaluation of forestry resources
 192 7.3. Counting a population of flamingos
 207 7.4. Counting the boats at a port
 229
 Chapter 8. Structure Extraction
 249 F. LAFARGE and X. DESCOMBES 8.1. Detection of the road network
 250 8.2. Extraction of building footprints
 262 8.3. Representation of natural textures
 269
 Chapter 9. Shape Recognition
 287 F. LAFARGE and C. MALLET 9.1. Modeling of a LIDAR signal
 287 9.2. 3D reconstruction of buildings
 308 Bibliography
 325 List of Authors
 341 Index 343.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781848212404 20160610
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TA1637 .S76 2012  Unknown 
5. Fourier methods in imaging [2010]
 Easton, Roger L., Jr.
 Chichester, West Sussex, U.K. : Wiley, 2010.
 Description
 Book — xxiv, 930 p. : ill. ; 26 cm.
 Summary

 Series Editor's Preface. Preface.
 1 Introduction. 1.1 Signals, Operators, and Imaging Systems. 1.2 The Three Imaging Tasks. 1.3 Examples of Optical Imaging. 1.4 ImagingTasks inMedical Imaging.
 2 Operators and Functions. 2.1 Classes of Imaging Operators. 2.2 Continuous and Discrete Functions. Problems.
 3 Vectors with RealValued Components. 3.1 Scalar Products. 3.2 Matrices. 3.3 Vector Spaces. Problems.
 4 Complex Numbers and Functions. 4.1 Arithmetic of Complex Numbers. 4.2 Graphical Representation of Complex Numbers. 4.3 Complex Functions. 4.4 Generalized Spatial Frequency  Negative Frequencies. 4.5 Argand Diagrams of ComplexValued Functions. Problems.
 5 ComplexValued Matrices and Systems. 5.1 Vectors with ComplexValued Components. 5.2 Matrix Analogues of ShiftInvariant Systems. 5.3 Matrix Formulation of ImagingTasks. 5.4 Continuous Analogues of Vector Operations. Problems.
 6 1D Special Functions. 6.1 Definitions of 1D Special Functions. 6.2 1D Dirac Delta Function. 6.3 1D ComplexValued Special Functions. 6.4 1D Stochastic FunctionsNoise. 6.5 Appendix A: Area of SINC [ x ] and SINC 2[ x ]. 6.6 Appendix B: Series Solutions for Bessel Functions J 0[ x ] and J 1[ x ]. Problems.
 7 2D Special Functions. 7.1 2D Separable Functions. 7.2 Definitions of 2D Special Functions. 7.3 2D Dirac Delta Function and its Relatives. 7.4 2D Functions with Circular Symmetry. 7.5 ComplexValued 2D Functions. 7.6 Special Functions of Three (orMore) Variables. Problems.
 8 Linear Operators. 8.1 Linear Operators. 8.2 ShiftInvariant.Operators. 8.3 Linear ShiftInvariant (LSI) Operators. 8.4 Calculating Convolutions. 8.5 Properties of Convolutions. 8.6 Autocorrelation. 8.7 Crosscorrelation. 8.8 2DLSIOperations. 8.9 Crosscorrelations of 2D Functions. 8.10 Autocorrelations of 2D.Functions. Problems.
 9 Fourier Transforms of 1D Functions. 9.1 Transforms of ContinuousDomain Functions. 9.2 Linear Combinations of Reference Functions. 9.3 ComplexValued Reference Functions. 9.4 Transforms of ComplexValued Functions. 9.5 Fourier Analysis of Dirac Delta Functions. 9.6 Inverse Fourier Transform. 9.7 Fourier Transforms of 1D Special Functions. 9.8 Theorems of the Fourier Transform. 9.9 Appendix: Spectrum of Gaussian via Path Integral. Problems.
 10 Multidimensional Fourier Transforms. 10.1 2D Fourier Transforms. 10.2 Spectra of Separable 2D Functions. 10.3 Theorems of 2D Fourier Transforms. Problems.
 11 Spectra of Circular Functions. 11.1 The Hankel Transform. 11.2 Inverse Hankel Transform. 11.3 Theorems of Hankel Transforms. 11.4 Hankel Transforms of Special Functions. 11.5 Appendix: Derivations of Equations (11.12) and (11.14). Problems.
 12 The Radon Transform. 12.1 LineIntegral Projections onto Radial Axes. 12.2 Radon Transforms of Special Functions. 12.3 Theorems of the Radon Transform. 12.4 Inverse Radon Transform. 12.5 CentralSlice Transform. 12.6 Three Transforms of Four Functions. 12.7 Fourier and Radon Transforms of Images. Problems.
 13 Approximations to Fourier Transforms. 13.1 Moment Theorem. 13.2 1D Spectra via Method of Stationary Phase. 13.3 CentralLimit Theorem. 13.4 Width Metrics and Uncertainty Relations. Problems.
 14 Discrete Systems, Sampling, and Quantization. 14.1 Ideal Sampling. 14.2 Ideal Sampling of Special Functions. 14.3 Interpolation of Sampled Functions. 14.4 WhittakerShannon Sampling Theorem. 14.5 Aliasingand Interpolation. 14.6 "Prefiltering" to Prevent Aliasing. 14.7 Realistic Sampling. 14.8 Realistic Interpolation. 14.9 Quantization. 14.10 Discrete Convolution. Problems.
 15 Discrete Fourier Transforms. 15.1 Inverse of the InfiniteSupport DFT. 15.2 DFT over Finite Interval. 15.3 Fourier Series Derived from Fourier Transform. 15.4 Efficient Evaluation of the Finite DFT. 15.5 Practical Considerations for DFT and FFT. 15.6 FFTs of 2D Arrays. 15.7 Discrete Cosine Transform. Problems.
 16 Magnitude Filtering. 16.1 Classes of Filters. 16.2 Eigenfunctions of Convolution. 16.3 Power Transmission of Filters. 16.4 Lowpass Filters. 16.5 Highpass Filters. 16.6 Bandpass Filters. 16.7 Fourier Transform as a Bandpass Filter. 16.8 Bandboost and Bandstop Filters. 16.9 Wavelet Transform. Problems.
 17 Allpass (Phase) Filters. 17.1 PowerSeries Expansion for Allpass Filters. 17.2 ConstantPhase Allpass Filter. 17.3 LinearPhase Allpass Filter. 17.4 QuadraticPhase Filter. 17.5 Allpass Filters with HigherOrder Phase. 17.6 Allpass RandomPhase Filter. 17.7 Relative Importance of Magnitude and Phase. 17.8 Imaging of Phase Objects. 17.9 Chirp Fourier Transform. Problems.
 18 MagnitudePhase Filters. 18.1 Transfer Functions of Three Operations. 18.2 Fourier Transform of Ramp Function. 18.3 Causal Filters. 18.4 Damped Harmonic Oscillator. 18.5 Mixed Filters with Linear or Random Phase. 18.6 Mixed Filter with Quadratic Phase. Problems.
 19 Applications of Linear Filters. 19.1 Linear Filters for the Imaging Tasks. 19.2 Deconvolution "Inverse Filtering". 19.3 Optimum Estimators for Signals in Noise. 19.4 Detection of Known Signals  Matched Filter. 19.5 Analogies of Inverse and Matched Filters. 19.6 Approximations to Reciprocal Filters. 19.7 Inverse Filtering of ShiftVariant Blur. Problems.
 20 Filtering in Discrete Systems. 20.1 Translation, Leakage, and Interpolation. 20.2 Averaging Operators Lowpass Filters. 20.3 Differencing Operators  Highpass Filters. 20.4 Discrete Sharpening Operators. 20.5 2DGradient. 20.6 Pattern Matching. 20.7 Approximate Discrete Reciprocal Filters. Problems.
 21 Optical Imaging in Monochromatic Light. 21.1 Imaging Systems Based on Ray Optics Model. 21.2 Mathematical Model of Light Propagation. 21.3 Fraunhofer Diffraction. 21.4 Imaging System based on Fraunhofer Diffraction. 21.5 Transmissive Optical Elements. 21.6 Monochromatic Optical Systems. 21.7 ShiftVariant Imaging Systems. Problems.
 22 Incoherent Optical Imaging Systems. 22.1 Coherence. 22.2 Polychromatic Source  Temporal Coherence. 22.3 Imaging in Incoherent Light. 22.4 System Function in Incoherent Light. Problems.
 23 Holography. 23.1 Fraunhofer Holography. 23.2 Holography in Fresnel Diffraction Region. 23.3 ComputerGenerated Holography. 23.4 Matched Filtering with CellType CGH. 23.5 SyntheticAperture Radar (SAR). Problems. References. Index.
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(source: Nielsen Book Data) 9780470689837 20160604
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TA1637 .E23 2010  Unknown 
 2nd ed.  Amsterdam ; Boston : Morgan Kaufmann/Elsevier, c2010.
 Description
 Book — xviii, 650 p. : ill. (chiefly col.) ; 25 cm.
 Summary

This landmark book is the first to describe HDRI technology in its entirety and covers a widerange of topics, from capture devices to tone reproduction and imagebased lighting. The techniques described enable you to produce images that have a dynamic range much closer to that found in the real world, leading to an unparalleled visual experience. As both an introduction to the field and an authoritative technical reference, it is essential to anyone working with images, whether in computer graphics, film, video, photography, or lighting design. New material includes chapters on High Dynamic Range Video Encoding, High Dynamic Range Image Encoding, and High Dynammic Range Display Devices. It is written by the inventors and initial implementors of High Dynamic Range Imaging. It covers the basic concepts (including just enough about human vision to explain why HDR images are necessary), image capture, image encoding, file formats, display techniques, tone mapping for lower dynamic range display, and the use of HDR images and calculations in 3D rendering. The range and depth of coverage is good for the knowledgeable researcher as well as those who are just starting to learn about High Dynamic Range imaging.
(source: Nielsen Book Data) 9780123749147 20160614
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TR594 .H54 2010  Unknown 
 London, UK : ISTE ; Hoboken, NJ : Wiley, 2010.
 Description
 Book — xix, 507 p. : ill., maps ; 24 cm.
 Summary

 Preface xv PART I. FOUNDATIONS
 1
 Chapter 1. Introduction to Mathematical Morphology
 3 Laurent NAJMAN, Hugues TALBOT 1.1. First steps with mathematical morphology: dilations and erosions
 4 1.2. Morphological filtering
 12 1.3. Residues
 22 1.4. Distance transform, skeletons and granulometric curves
 24 1.5. Hierarchies and the watershed transform
 30 1.6. Some concluding thoughts
 33
 Chapter 2. Algebraic Foundations of Morphology
 35 Christian RONSE, Jean SERRA 2.1. Introduction
 35 2.2. Complete lattices
 36 2.3. Examples of lattices
 42 2.4. Closings and openings
 51 2.5. Adjunctions
 56 2.6. Connections and connective segmentation
 64 2.7. Morphological filtering and hierarchies
 75 Chapter 3.Watersheds in Discrete Spaces
 81 Gilles BERTRAND, Michel COUPRIE, Jean COUSTY, Laurent NAJMAN 3.1. Watersheds on the vertices of a graph
 82 3.2. Watershed cuts: watershed on the edges of a graph
 90 3.3. Watersheds in complexes
 101 PART II. EVALUATING AND DECIDING
 109
 Chapter 4. An Introduction to Measurement Theory for Image Analysis
 111 Hugues TALBOT, Jean SERRA, Laurent NAJMAN 4.1. Introduction
 111 4.2. General requirements
 112 4.3. Convex ring and Minkowski functionals
 113 4.4. Stereology and Minkowski functionals
 119 4.5. Change in scale and stationarity
 121 4.6. Individual objects and granulometries
 122 4.7. Graylevel extension
 128 4.8. As a conclusion
 130
 Chapter 5. Stochastic Methods
 133 Christian LANTUEJOUL 5.1. Introduction
 133 5.2. Random transformation
 134 5.3. Random image
 138
 Chapter 6. Fuzzy Sets and Mathematical Morphology
 155 Isabelle BLOCH 6.1. Introduction
 155 6.2. Background to fuzzy sets
 156 6.3. Fuzzy dilations and erosions from duality principle
 160 6.4. Fuzzy dilations and erosions from adjunction principle
 165 6.5. Links between approaches
 167 6.6. Application to the definition of spatial relations
 170 6.7. Conclusion
 176 PART III. FILTERING AND CONNECTIVITY
 177
 Chapter 7. Connected Operators based on Tree Pruning Strategies
 179 Philippe SALEMBIER 7.1. Introduction
 179 7.2. Connected operators
 181 7.3. Tree representation and connected operator
 182 7.4. Tree pruning
 187 7.5. Conclusions
 198
 Chapter 8. Levelings
 199 Jean SERRA, Corinne VACHIER, Fernand MEYER 8.1. Introduction
 199 8.2. Settheoretical leveling
 200 8.3. Numerical levelings
 209 8.4. Discrete levelings
 214 8.5. Bibliographical comment
 227
 Chapter 9. Segmentation, Minimum Spanning Tree and Hierarchies
 229 Fernand MEYER, Laurent NAJMAN 9.1. Introduction
 229 9.2. Preamble: watersheds, floodings and plateaus
 230 9.3. Hierarchies of segmentations
 237 9.4. Computing contours saliency maps
 252 9.5. Using hierarchies for segmentation
 255 9.6. Lattice of hierarchies
 258 PART IV. LINKS AND EXTENSIONS
 263
 Chapter 10. Distance, Granulometry and Skeleton
 265 Michel COUPRIE, Hugues TALBOT 10.1. Skeletons
 265 10.2. Skeletons in discrete spaces
 269 10.3. Granulometric families and skeletons
 270 10.4. Discrete distances
 275 10.5. Bisector function
 279 10.6. Homotopic transformations
 280 10.7. Conclusion
 289
 Chapter 11. Color and Multivariate Images
 291 Jesus ANGULO, Jocelyn CHANUSSOT 11.1. Introduction
 291 11.2. Basic notions and notation
 292 11.3. Morphological operators for color filtering
 299 11.4. Mathematical morphology and color segmentation
 312 11.5. Conclusion
 320
 Chapter 12. Algorithms for Mathematical Morphology
 323 Thierry GERAUD, Hugues TALBOT, Marc VAN DROOGENBROECK 12.1. Introduction
 323 12.2. Translation of definitions and algorithms
 324 12.3. Taxonomy of algorithms
 329 12.4. Geodesic reconstruction example
 334 12.5. Historical perspectives and bibliography notes
 344 12.6. Conclusions
 352 PART V. APPLICATIONS
 355
 Chapter 13. Diatom Identification with Mathematical Morphology
 357 Michael WILKINSON, Erik URBACH, Andre JALBA, Jos ROERDINK 13.1. Introduction
 357 13.2. Morphological curvature scale space
 358 13.3. Scalespace feature extraction
 359 13.4. 2D sizeshape pattern spectra
 359 13.5. Datasets
 364 13.6. Results
 364 13.7. Conclusions
 365
 Chapter 14. Spatiotemporal Cardiac Segmentation
 367 Jean COUSTY, Laurent NAJMAN, Michel COUPRIE 14.1.Which objects of interest?
 368 14.2. How do we segment?
 369 14.3. Results, conclusions and perspectives
 372
 Chapter 15. 3D Angiographic Image Segmentation
 375 Benoit NAEGEL, Nicolas PASSAT, Christian RONSE 15.1. Context
 375 15.2. Anatomical knowledge modeling
 376 15.3. Hitormiss transform
 378 15.4. Application: two vessel segmentation examples
 378 15.5. Conclusion
 383
 Chapter 16. Compression
 385 Beatriz MARCOTEGUI, Philippe SALEMBIER 16.1. Introduction
 385 16.2. Morphological multiscale decomposition
 385 16.3. Regionbased decomposition
 389 16.4. Conclusions
 391
 Chapter 17. Satellite Imagery and Digital ElevationModels
 393 Pierre SOILLE 17.1. Introduction
 393 17.2. On the specificity of satellite images
 394 17.3. Mosaicing of satellite images
 398 17.4. Applications to digital elevation models
 400 17.5. Conclusion and perspectives
 405
 Chapter 18. Document Image Applications
 407 Dan BLOOMBERG, Luc VINCENT 18.1. Introduction
 407 18.2. Applications
 409
 Chapter 19. Analysis and Modeling of 3D Microstructures
 421 Dominique JEULIN 19.1. Introduction
 421 19.3. Models of random multiscale structures
 431 19.4. Digital materials
 440 19.5. Conclusion
 444
 Chapter 20. Random Spreads and Forest Fires
 445 Jean SERRA 20.1. Introduction
 445 20.2. Random spread
 448 20.3. Forecast of the burnt zones
 451 20.4. Discussion: estimating and choosing
 453 20.5. Conclusions
 454 Bibliography
 457 List of Authors
 499 Index 501.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781848212152 20160604
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TA1637 .M35963 2010  Unknown 
8. Multivariate image processing [2010]
 London : ISTE ; Hoboken, NJ : Wiley, 2010.
 Description
 Book — xvi, 459 p., [4] p. of plates : ill. (some col.) ; 24 cm.
 Summary

 1. Introduction to Multivariate Image Processing from the Basics to New Challenges.
 2. Registration.
 3. Fusion of SAR and Optical Data.
 4. Fusion of Satellite Images at Different Resolutions.
 5. Multitemporal Processing and Change Detection.
 6. Bayesian Approach to Linear Spectral Mixture Analysis.
 7. Detection and Tracking of Emission Rays in Radioastronomy.
 8. Wavelet Transform for the Denoising of Multivariate Images.
 9. Bayesian Approach for Polarizationencoded Image Analysis.
 10. Unsupervised Classification for Multivariate Images.
 11. Noise Estimation.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781848211391 20160528
 Online
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TA1637 .M885 2010  Unknown 
 Starck, J.L. (JeanLuc), 1965
 Cambridge [England] ; New York : Cambridge University Press, 2010.
 Description
 Book — xvii, 316 p., [16] p. of plates : ill. (some col.) ; 27 cm.
 Summary

 1. Introduction to the world of sparsity
 2. The wavelet transform
 3. Redundant wavelet transform
 4. Nonlinear multiscale transforms
 5. The ridgelet and curvelet transforms
 6. Sparsity and noise removal
 7. Linear inverse problems
 8. Morphological diversity
 9. Sparse blind source separation
 10. Multiscale geometric analysis on the sphere
 11. Compressed sensing.
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(source: Nielsen Book Data) 9780521119139 20160604
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QA601 .S785 2010  Unknown 
 Ohser, Joachim.
 Weinheim [Germany] : WileyVCH, c2009.
 Description
 Book — xvi, 325 p. : ill. (some col.) ; 25 cm.
 Summary

 Foreword. Preface. Conventions and Notation.
 1 Introduction.
 2 Preliminaries. 2.1 General Notation. 2.2 Characteristics of Sets. 2.3 Random Sets. 2.4 Fourier Analysis.
 3 Lattices, Adjacency of Lattice Points, and Images. 3.1 Introduction. 3.2 Point Lattices, Digitizations and Pixel Configurations. 3.3 Adjacency and Euler Number. 3.4 The Euler Number of Microstructure Constituents. 3.5 Image Data. 3.6 Rendering.
 4 Image Processing. 4.1 Fourier Transform of an Image. 4.2 Filtering. 4.3 Segmentation.
 5 Measurement of Intrinsic Volumes and Related Quantities. 5.1 Introduction. 5.2 Intrinsic Volumes. 5.3 Intrinsic Volume Densities. 5.4 Directional Analysis. 5.5 Distances Between Random Sets and Distance Distributions.
 6 Spectral Analysis. 6.1 Introduction. 6.2 SecondOrder Characteristics of a Random Volume Measure. 6.3 Correlations Between Random Structures. 6.4 SecondOrder Characteristics of Random Surfaces. 6.5 SecondOrder Characteristics of Random Point Fields.
 7 Modelbased Image Analysis. 7.1 Introduction, Motivation. 7.2 Point Field Models. 7.3 Macroscopically Homogeneous Systems of Nonoverlapping Particles. 7.4 Macroscopically Homogeneous Systems of Overlapping Particles. 7.5 Macroscopically Homogeneous Fibre Systems. 7.6 Tessellations.
 8 Simulation of Material Properties. 8.1 Introduction. 8.2 Effective Conductivity of Polycrystals by Stochastic Homogenization. 8.3 Computation of Effective Elastic Moduli of Porous Media by FEM Simulation. References. Index.
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(source: Nielsen Book Data) 9783527312030 20160528
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TA418.23 .O37 2009  Unknown 
 Brunelli, Roberto, 1961
 Chichester, West Sussex, U.K. : Wiley, 2009.
 Description
 Book — x, 338 p. : ill. ; 25 cm.
 Summary

 Preface.
 1 Introduction. 1.1 Template Matching and Computer Vision. 1.2 The Book. 1.3 Bibliographical Remarks. References.
 2 The Imaging Process. 2.1 Image Creation. 2.1.1 Light. 2.1.2 Gathering Light. 2.1.3 Diffractionlimited Systems. 2.1.4 Quantum Noise. 2.2 Biological Eyes. 2.2.1 The Human Eye. 2.2.2 Alternative Designs. 2.3 Digital Eyes. 2.4 Digital Image Representations. 2.4.1 TheSampling Theorem. 2.4.2 Image Resampling. 2.4.3 Logpolar Mapping. 2.5 Bibliographical Remarks. References.
 3 Template Matching as Testing. 3.1 Detectionand Estimation. 3.2 Hypothesis Testing. 3.2.1 The Bayes RiskCriterion. 3.2.2 The NeymanPearson Criterion. 3.3 An Important Example. 3.4 A Signal Processing Perspective: Matched Filters. 3.5 Pattern Variability and the Normalized Correlation Coefficient. 3.6 Estimation. 3.6.1 Maximum Likelihood Estimation. 3.6.2 Bayes Estimation. 3.6.3 JamesStein Estimation. 3.7 Bibliographical Remarks. References.
 4 Robust Similarity Estimators. 4.1 Robustness Measures. 4.2 Mestimators. 4.3 L1 Similarity Measures. 4.4 Robust Estimation of Covariance Matrices. 4.5 Bibliographical Remarks. References.
 5 Ordinal Matching Measures. 5.1 Ordinal Correlation Measures. 5.1.1 Spearman Rank Correlation. 5.1.2 Kendall Correlation. 5.1.3 BhatNayar Correlation. 5.2 Nonparametric Local Transforms. 5.2.1 The Census and Rank Transforms. 5.2.2 Incremental Sign Correlation. 5.3 Bibliographical Remarks. References.
 6 Matching Variable Patterns. 6.1 Multiclass Synthetic Discriminant Functions. 6.2 Advanced Synthetic Discriminant Functions. 6.3 Nonorthogonal Image Expansion. 6.4 Bibliographical Remarks. References.
 7 Matching Linear Structure: The Hough Transform. 7.1 Getting Shapes: Edge Detection. 7.2 The Radon Transform. 7.3 The Hough Transform: Line and Circle Detection. 7.4 The Generalized Hough Transform. 7.5 Bibliographical Remarks. References.
 8 Lowdimensionality Representations and Matching. 8.1 Principal Components. 8.1.1 Probabilistic PCA. 8.1.2 How Many Components? 8.2 ANonlinear Approach: Kernel PCA. 8.3 Independent Components. 8.4 Linear Discriminant Analysis. 8.4.1 Bayesian Dual Spaces. 8.5 A Sample Application: Photographicquality Facial Composites. 8.6 Bibliographical Remarks. References.
 9 Deformable Templates. 9.1 A Dynamic Perspective on the Hough Transform. 9.2 Deformable Templates. 9.3 Active Shape Models. 9.4 DiffeomorphicMatching. 9.5 Bibliographical Remarks. References.
 10 Computational Aspects of Template Matching. 10.1 Speed. 10.1.1 Early Jumpout. 10.1.2 TheUse of SumTables. 10.1.3 Hierarchical Template Matching. 10.1.4 Metric Inequalities. 10.1.5 The FFT Advantage. 10.1.6 PCAbasedSpeedup. 10.1.7 A Combined Approach. 10.2 Precision. 10.2.1 A Perturbative Approach. 10.2.2 Phase Correlation. 10.3 Bibliographical Remarks. References.
 11 Matching Point Sets: The Hausdorff Distance. 11.1 Metric Pattern Spaces. 11.2 Hausdorff Matching. 11.3 Efficient Computation of the Hausdorff Distance. 11.4 Partial Hausdorff Matching. 11.5 Robustness Aspects. 11.6 A Probabilistic Perspective. 11.7 Invariant Moments. 11.8 Bibliographical Remarks. References.
 12 Support Vector Machines and Regularization Networks. 12.1 Learning and Regularization. 12.2 RBF Networks. 12.2.1 RBF Networks for Gender Recognition. 12.3 Support Vector Machines. 12.3.1 Improving Efficiency. 12.3.2 Multiclass SVMs. 12.3.3 Best Practice. 12.4 Bibliographical Remarks. References.
 13 Feature Templates. 13.1 Detecting Templates by Features. 13.2 Parametric FeatureManifolds. 13.3 Multiclass Pattern Rejection. 13.4 Template Features. 13.5 Bibliographical Remarks. References.
 14 Building a Multibiometric System. 14.1 Systems. 14.2 The Electronic Librarian. 14.3 Score Integration. 14.4 Rejection. 14.5 Bibliographical Remarks. References. Appendices. A AnImAl: A Software Environment for Fast Prototyping. A.1 AnImAl: An Image Algebra. A.2 Image Representationand Processing Abstractions. A.3 The AnImAl Environment. A.4 Bibliographical Remarks. References. B Synthetic Oracles for Algorithm Development. B.1 Computer Graphics. B.2 Describing Reality: Flexible Rendering Languages. B.3 Bibliographical Remarks. References. C On Evaluation. C.1 A Note on Performance Evaluation. C.2 Traininga Classifier. C.3 Analyzing the Performance of a Classifier. C.4 Evaluating a Technology. C.5 Bibliographical Remarks. References. Index.
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(source: Nielsen Book Data) 9780470517062 20160528
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TA1634 .B78 2009  Unknown 
12. Digital color image processing [2008]
 Koschan, Andreas, 1956
 Hoboken, N.J. : WileyInterscience, c2008.
 Description
 Book — xv, 375 p. : ill. (chiefly col.) ; 25 cm.
 Summary

 1. Introduction.1.1 Goal and Content of This Book.1.2 Terminology in Color Image Processing.1.2.1 What Is A Digital Color Image?1.2.2 Derivative of a Color Image.1.2.3 Color Edges.1.2.4 Color Constancy.1.2.5 Contrast of a Color Image.1.2.6 Noise in Color Images.1.2.7 Luminance, Illuminance, and Brightness.1.3 Color Image Analysis in Practical Use.1.4 References to Further Reading.1.5 References.2. Eye and Color.2.1 Physiology of Color Vision.2.2 Receptoral Color Information.2.3 Postreceptoral Color Information.2.4 Cortical Color Information.2.5 References.3. Color Spaces and Color Distances.3.1 Standard Color System.3.1.1 CIE Color Matching Functions.3.1.2 Standard Color Values.3.1.3 Chromaticity Diagrams.3.1.4 Macadam Ellipses 48.3.2 Physics And TechnicsBased Color Spaces.3.2.1 RRGBb Color Spaces.3.2.2 CMY(K) Color Space.3.2.3 YIQ Color Space.3.2.4 YUV Color Space.3.2.5 YCBCRColor Space.3.2.6 Kodak PhotoCD YC1C2 Color Space.3.2.7 I1I2I3 Color Space.3.3 Uniform Color Spaces.3.3.1 CIELAB Color Space.3.3.2 CIELUV Color Space.3.4 PerceptionBased Color Spaces.3.4.1 HSI Color Space.3.4.2 HSV Color Space.3.4.3 Opponent Color Spaces.3.5 Color Difference Formulae.3.5.1 Color Difference Formulae in the RGB Color Space.3.5.2 Color Difference Formulae in the HSI Color Space.3.5.3 Color Difference Formulae in the CIELAB and CIELUV Color Spaces.3.6 Color Ordering Systems.3.6.1 Munsell Color System.3.6.2 MacbethColorchecker.3.6.3 DIN Color Map.3.7 References.4. Color Image Formation.4.1 Technical Design of Electronic Color Cameras.4.1.1 Image Sensors.4.1.2 Multispectral Imaging Using Black And White Cameras with Color Filters.4.1.3 OneChip CCD Color Camera.4.1.4 ThreeChip CCD Color Cameras.4.1.5 Digital Cameras.4.2 Standard Color Filters and Standard Illuminants.4.2.1 Standard Color Filters.4.2.2 Standard Illuminants.4.3 Photometric Sensor Model.4.3.1 Attenuation, Clipping, and Blooming.4.3.2 Chromatic Aberration 93.4.3.3 Correction of the Chromatic Aberration.4.4 Photometric and Colorimetric Calibration.4.4.1 Nonlinearities of Camera Signals.4.4.2 Measurement of Camera Linearity.4.4.3 White Balance and Black Level Determination.4.4.4 Transformation into the Standard Color System XYZ.4.5 References.5. Color Image Enhancement.5.1 False Colors and Pseudo Colors.5.2 Enhancement of Real Color Images.5.3 Noise Removal in Color Images.5.3.1 BoxFilter.5.3.2 Median Filter.5.3.3 Morphological Filter.5.3.4 Filtering In the Frequency Domain.5.4 Contrast Enhancement in Color Images.5.4.1 Treatment of Color Saturation and Lightness.5.4.2 Changing the Hue.5.5 References.6. Edge Detection in Color Images.6.1 VectorValued Techniques.6.1.1 Color Variants of the Canny Operator.6.1.2 Cumani Operator.6.1.3 Operators Based On Vector Order Statistics.6.2 Results of Color Edge Operators.6.3 Classification of Edges.6.3.1 PhysicsBased Classification.6.3.2 Classification Applying Photometric Invariant Gradients.6.4 Color Harris Operator.6.4 References.7. Color Image Segmentation.7.1 PixelBased Segmentation.7.1.1 Histogram Techniques.7.1.2 Cluster Analysis in the Color Space.7.2 AreaBased Segmentation.7.2.1 Region Growing Technique.7.2.2 Split and Merge.7.3 EdgeBased Segmentation.7.3.1 Local Techniques.7.3.2 Segmentation by Watershed Transformation.7.4 PhysicsBased Segmentation.7.4.1 Dichromatic Reflection Model.7.4.2 Classification Techniques.7.5 Comparison of Segmentation Processes.7.6 References.8. Highlights, Interreflections, and Color Constancy.8.1 Highlight Analysis in Color Images.8.1.1 KlinkerShaferKanade Technique.8.1.2 TongFunt Technique.8.1.3 GershonJepsonTsotsos Technique.8.1.4 SchlNsTeschner Technique.8.1.5 Spectral Differencing Using Several Images.8.1.6 Photometric MultiImage Technique.8.1.7 Polarization Technique.8.2 Interreflection Analysis in Color Images.8.2.1 OneBounce Model for Interreflections.8.2.2 Minimization of Interreflections in Real Color Images.8.3 Color Constancy.8.3.1 A Mathematical Formulation of the Color Constancy Problem.8.3.2 Techniques for Color Constancy.8.4 References.9. Static Stereo Analysis in Color Images.9.1 Geometry of a Stereo Image Acquisition System.9.2 AreaBased Correspondence Analysis.9.2.1 Dense Disparity Maps by Chromatic BlockMatching.9.2.3 Hierarchic BlockMatching In a Color Image Pyramid.9.2.4 Stereo Analysis with Color Pattern Projection.9.3 FeatureBased Correspondence Analysis.9.3.1 EdgeBased Correspondence Analysis.9.3.2 General Ideas.9.4 References.10. Dynamic and Photometric Stereo Analyses in Color Images.10.1 Optical Flow.10.1.1 Solutions.10.1.2 HornSchunck Constraint for Color Image Sequences.10.2 Photometric Stereo Analysis.10.2.1 Photometric Stereo Analysis for NonStatic Scenes.10.2.2 Photometric Stereo Analysis for NonLambertian Surfaces.10.3 References.11. ColorBased Tracking with PTZ Cameras.11.1 The Background Problem.11.2 Methods for Tracking.11.2.1 Active Shape Models.11.2.2 Automatic Target Acquisition and Handover from Fixed To PTZ Camera.11.2.3 Color and Predicted Direction and Speed of Motion.11.3 Technical Aspects of Tracking.11.3.1 Feature Extraction for Zooming and Tracking.11.3.2 Color Extraction from a Moving Target.11.4 Color Active Shape Models.11.4.1 Landmark Points.11.4.2 Principal Component Analysis.11.4.3 Model Fitting.11.4.4 Modeling a Local Structure.11.4.5 Hierarchical Approach for MultiResolution ASM.11.4.6 Extending ASMS to Color Image Sequences.11.4.7 Selecting the Number of Landmark Points.11.4.8 Partial Occlusions.11.4.9 Summary.11.5 References.12. Multispectral Imaging for Biometrics.12.1 What Is A Multispectral Image?.12.2 Multispectral Image Acquisition.12.3 Fusion Of Visible And Infrared Images For Face Recognition.12.3.1 Registration Of Visible And Thermal Face Images.12.3.2 Empirical Mode Decomposition.12.3.3 Image Fusion Using EMD.12.3.4 Experimental Results.12.4 Multispectral Image Fusion in the Visible Spectrum for Face Recognition.12.4.1 PhysicsBased Weighted Fusion.12.4.2 Illumination Adjustment Via Data Fusion.12.4.3 Wavelet Fusion.12.4.4 Cmc Measure.12.4.5 Multispectral, Multimodal and MultiIlluminant IrRISM3 12.4.6 Experimental Results.12.5 References.13. PseudoColoring in Single Energy XRay Images.13.1 Problem Statement.13.2 Aspects of The Human Perception Of Color.13.2.1 Physiological Processing Of Color.12.2.2 Psychological Processing Of Color.12.2.3 General Recommendations for Optimum Color Assignment.13.2.4 PhysiologicallyBased Guidelines.13.2.5 PsychologicallyBased Guidelines.13.3 Theoretical Aspects of PseudoColoring.13.4 RGBBased Color Maps.13.4.1 PerceptuallyBased Color Maps.Linear Mapping.NonLinear Mapping.13.4.2 Mathematical Formulations.Algebraic Transforms.Sine/Cosine Transform.Rainbow Transform.13.5 HSI Based Color Maps.13.5.1 Mapping of Raw Grayscale Data.HistogramBased ColorMapping.FunctionBased Mapping.13.5.2 Color Applied To Preprocessed Gray Scale Data.Constant Saturation.Variable Saturation (VS).13.6 Experimental Results.13.6.1 ColorCoded Images Generated By RGBBased Transforms.13.6.2 ColorCoded Images Generated By HSIBased Transforms.13.7 Performance Evaluation.13.7.1 Preliminary OnLine Survey.13.7.2 Formal Airport Evaluation.13.8 Conclusion.13.9 References.
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(source: Nielsen Book Data) 9780470147085 20160528
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TA1637 .K678 2008  Unknown 
13. Digital image processing [2008]
 Gonzalez, Rafael C.
 3rd ed.  Upper Saddle River, NJ : Pearson/Prentice Hall, c2008.
 Description
 Book — xxii, 954 p. : ill. (some col.) ; 25 cm.
 Summary

 DRAFT Chapters end with a Summary, References and Further Reading, and Problems.
 1. Introduction. What Is Digital Image Processing? The Origins of Digital Image Processing. Examples of Fields that Use Digital Image Processing. Fundamental Steps in Digital Image Processing. Components of an Image Processing System.
 2. Digital Image Fundamentals. Elements of Visual Perception. Light and the Electromagnetic Spectrum. Image Sensing and Acquisition. Image Sampling and Quantization. Some Basic Relationships Between Pixels. Linear and Nonlinear Operations.
 3. Image Enhancement in the Spatial Domain. Background. Some Basic Gray Level Transformations. Histogram Processing. Enhancement Using Arithmetic/Logic Operations. Basics of Spatial Filtering. Smoothing Spatial Filters. Sharpening Spatial Filters. Combining Spatial Enhancement Methods.
 4. Image Enhancement in the Frequency Domain. Background. Introduction to the Fourier Transform and the Frequency Domain. Smoothing FrequencyDomain Filters. Sharpening Frequency Domain Filters. Homomorphic Filtering. Implementation.
 5. Image Restoration. A Model of the Image Degradation/Restoration Process. Noise Models. Restoration in the Presence of Noise OnlySpatial Filtering. Periodic Noise Reduction by Frequency Domain Filtering. Linear, PositionInvariant Degradations. Estimating the Degradation Function. Inverse Filtering. Minimum Mean Square Error (Wiener) Filtering. Constrained Least Squares Filtering. Geometric Mean Filter. Geometric Transformations.
 6. Color Image Processing. Color Fundamentals. Color Models. Pseudocolor Image Processing. Basics of FullColor Image Processing. Color Transformations. Smoothing and Sharpening. Color Segmentation. Noise in Color Images. Color Image Compression.
 7. Wavelets and Multiresolution Processing. Background. Multiresolution Expansions. Wavelet Transforms in One Dimension. The Fast Wavelet Transform. Wavelet Transforms in Two Dimensions. Wavelet Packets.
 8. Image Compression. Fundamentals. Image Compression Models. Elements of Information Theory. ErrorFree Compression. Lossy Compression. Image Compression Standards.
 9. Morphological Image Processing. Preliminaries. Dilation and Erosion. Opening and Closing. The HitorMiss Transformation. Some Basic Morphological Algorithms. Extensions to GrayScale Images.
 10. Image Segmentation. Detection of Discontinuities. Edge Linking and Boundary Detection. Thresholding. RegionBased Segmentation. Segmentation by Morphological Watersheds. The Use of Motion in Segmentation.
 11. Representation and Description. Representation. Boundary Descriptors. Regional Descriptors. Use of Principal Components for Description. Relational Descriptors.
 12. Object Recognition. Patterns and Pattern Classes. Recognition Based on DecisionTheoretic Methods. Structural Methods.
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(source: Nielsen Book Data) 9780135052679 20160527
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TA1632 .G66 2008  Unknown 
 Burger, Wilhelm.
 1st ed.  New York : Springer, c2008.
 Description
 Book — xx, 564 p. : ill. (some col.) ; 27 cm.
 Summary

 Crunching Pixels. Digital Images. ImageJ. Histograms. Point Operations. Filters. Edges and Contours. Corner Dectection. Detecting Simple Curves. Morphological Filters. Regions in Binary Images. Color Images. Introduction to Spectral Techniques. The Discrete Fourier Transform in 2D. The Discrete Cosine Transform (DCT). Geometrical Operations. Comparing Images. A Mathematical Notation. B Java Notes. C ImageJ Short Reference. D Source Code. References. Index.
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(source: Nielsen Book Data) 9781846283796 20160528
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TA1637 .B885 2008  Unknown 
 Sonka, Milan.
 3rd ed.  Toronto, Ont. : Thompson Learning, c2008.
 Description
 Book — xxv, 829 p., [8] p. of plates : ill. (some col.) ; 25 cm.
 Online
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TA1637 .S66 2008  Unknown 
16. Smart CMOS image sensors and applications [2008]
 Ohta, Jun.
 Boca Raton : CRC Press, c2008.
 Description
 Book — xvii, 248 p. : ill. ; 25 cm.
 Summary

 Introduction A general overview Brief history of CMOS image sensors Brief history of smart CMOS image sensors Organization of the book Fundamentals of CMOS Image Sensors Introduction Fundamental of photo detection Photo detectors for smart CMOS image sensors Accumulation mode in PD Basic pixel structures Sensor peripherals Basic sensor characteristics Color Pixel sharing Comparison between pixel architecture Comparison with CCDs Smart Functions and Materials Introduction Pixel structure Analog operation Pulse modulation Digital processing Materials other than Silicon Structures other than standard CMOS technologies Smart Imaging Introduction Low light imaging High speed Wide dynamic range Demodulation 3D range finder Target tracking Dedicated arrangement of pixel and optics Applications Introduction Information and communication applications Biotechnology applications Medical applications Appendix A: Tables of Constants Appendix B: Illuminance Appendix C: Human Eye and CMOS Image Sensors Appendix D: Fundamental Characteristics of MOS Capacitor Appendix E: Fundamental Characteristics of MOSFET Appendix F: Optical Format and Resolution References Index.
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(source: Nielsen Book Data) 9780849336812 20160528
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TK7871.99 .M44 O35 2008  Unknown 
 Campisi, Patrizio, 1968
 Boca Raton, Fla. : CRC Press, c2007.
 Description
 Book — 448 p., [4] p. of plates : ill. (some col.) ; 25 cm.
 Summary

 BLIND IMAGE DECONVOLUTION: PROBLEM FORMULATION AND EXISTING APPROACHES Tom E. Bishop, S. Derin Babacan, Bruno Amizic, Aggelos K. Katsaggelos, Tony Chan, and Rafael Molina Introduction Mathematical Problem Formulation Classification of Blind Image Deconvolution Methodologies Bayesian Framework for Blind Image Deconvolution Bayesian Modeling of Blind Image Deconvolution Bayesian Inference Methods in Blind Image Deconvolution NonBayesian Blind Image Deconvolution Models Conclusions References BLIND IMAGE DECONVOLUTION USING BUSSGANG TECHNIQUES: APPLICATIONS TO IMAGE DEBLURRING AND TEXTURE SYNTHESIS Patrizio Campisi, Alessandro Neri, Stefania Colonnese, Gianpiero Panci, and Gaetano Scarano Introduction Bussgang Processes SingleChannel Bussgang Deconvolution Multichannel Bussgang deconvolution Conclusions References BLIND MULTIFRAME IMAGE DECONVOLUTION USING ANISOTROPIC SPATIALLY ADAPTIVE FILTERING FOR DENOISING AND REGULARIZATION Vladimir Katkovnik, Karen Egiazarian, and Jaakko Astola Introduction Observation Model and Preliminaries Frequency Domain Equations Projection Gradient Optimization Anisotropic LPAICI Spatially Adaptive Filtering Blind Deconvolution Algorithm Identifiability and Convergence Simulations Conclusions Acknowledgments References BAYESIAN METHODS BASED ON VARIATIONAL APPROXIMATIONS FOR BLIND IMAGE DECONVOLUTION Aristidis Likas and Nikolas P. Galatsanos Introduction Background on Variational Methods Variational Blind Deconvolution Numerical Experiments Conclusions and Future Work APPENDIX A: Computation of the Variational Bound F(q, ?) APPENDIX B: Maximization of F(q, ?) References DECONVOLUTION OF MEDICAL IMAGES FROM MICROSCOPIC TO WHOLE BODY IMAGES Oleg V. Michailovich and Dan R. Adam Introduction Nonblind Deconvolution Blind Deconvolution in Ultrasound Imaging Blind Deconvolution in SPECT Blind Deconvolution in Confocal Microscopy Summary References BAYESIAN ESTIMATION OF BLUR AND NOISE IN REMOTE SENSING IMAGING Andre Jalobeanu, Josiane Zerubia, and Laure BlancFeraud Introduction The Forward Model Bayesian Estimation: Invert the Forward Model Possible Improvements and Further Development Results Conclusions Acknowledgments References DECONVOLUTION AND BLIND DECONVOLUTION IN ASTRONOMY Eric Pantin, Jeanluc Starck, and Fionn Murtagh Introduction The Deconvolution Problem Linear Regularized Methods CLEAN Bayesian Methodology Iterative Regularized Methods WaveletBased Deconvolution Deconvolution and Resolution Myopic and Blind Deconvolution Conclusions and Chapter Summary Acknowledgments References MULTIFRAME BLIND DECONVOLUTION COUPLED WITH FRAME REGISTRATION AND RESOLUTION ENHANCEMENT Filip A roubek, Jan Flusser, and Gabriel Cristobal Introduction Mathematical Model Polyphase Formulation Reconstruction of Volatile Blurs Blind Superresolution Experiments Conclusions Acknowledgments References BLIND RECONSTRUCTION OF MULTIFRAME IMAGERY BASED ON FUSION AND CLASSIFICATION Dimitrios Hatzinakos, Alexia Giannoula, and Jianxin Han Introduction System Overview Recursive Inverse Filtering with Finite NormalDensity Mixtures (RIFFNM) Optimal Filter Adaptation Effects of Noise The Fusion and Classification Recursive Inverse Filtering Algorithm (FACRIF) Experimental Results Final Remarks References BLIND DECONVOLUTION AND STRUCTURED MATRIX COMPUTATIONS WITH APPLICATIONS TO ARRAY IMAGING Michael K. Ng and Robert J. Plemmons Introduction OneDimensional Deconvolution Formulation Regularized and Constrained TLS Formulation Numerical Algorithms TwoDimensional Deconvolution Problems Numerical Examples Application: HighResolution Image Reconstruction Concluding Remarks and Current Work Acknowledgments References INDEX.
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(source: Nielsen Book Data) 9780849373671 20160528
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TA1632 .C35 2007  Unknown 
 Pratt, William K.
 4th ed.  Hoboken, N.J. : WileyInterscience, c2007.
 Description
 Book — xix, 782 p., [4] p. of plates : ill. (some col.) ; 25 cm. + 1 CDROM (4 3/4 in.)
 Summary

 Preface.Acknowledgments.
 PART 1 CONTINUOUS IMAGE CHARACTERIZATION.1 Continuous Image Mathematical Characterization.1.1 Image Representation.1.2 TwoDimensional Systems.1.3 TwoDimensional Fourier Transform.1.4 Image Stochastic Characterization.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 TWODIMENSIONAL PROCESSING.6 Discrete Image Mathematical Characterization.6.1 VectorSpace Image Representation.6.2 Generalized TwoDimensional 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 FiniteArea 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 KarhunenLoeve 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 MultiPlane 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 FirstOrder Derivative Edge Detection.15.3 SecondOrder Derivative Edge Detection.15.4 EdgeFitting 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 RegionofInterest 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 VectorSpace Algebra Concepts.
 Appendix 2 Color Coordinate Conversion.
 Appendix 3 Image Error Measures.
 Appendix 4 PIKS Compact Disk.Bibliography.Index.
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(source: Nielsen Book Data) 9780471767770 20160528
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TA1632 .P7 2007  Unknown 
19. The image processing handbook [2007]
 Russ, John C.
 5th ed.  Boca Raton, FL : CRC/Taylor and Francis, c2007.
 Description
 Book — 817 p. : ill. (some col.) ; 26 cm.
 Summary

 ACQUIRING IMAGESHuman Reliance on Images for InformationVideo CamerasCCD CamerasCamera Artifacts and LimitationsColor CamerasCamera ResolutionCMOS CamerasFocusingElectronics and Bandwidth LimitationsPixelsGrayScale ResolutionNoiseHighDepth ImagesColor ImagingDigital Camera LimitationsColor SpacesColor CorrectionColor DisplaysImage TypesRange ImagingMultiple ImagesStereoscopyImaging RequirementsHUMAN VISIONWhat We See and WhyRecognitionTechnical AspectsAcuityWhat the Eye Tells the BrainSpatial ComparisonsLocal to Global HierarchiesIt's About TimeThe Third DimensionHow versus WhatSeeing What Isn't There, and Vice VersaImage CompressionA World of LightSize MattersShape (Whatever That Means)ContextArrangements Must be MadeSeeing is BelievingSo, in ConclusionPRINTING AND STORAGEPrintingDots on PaperColor PrintingPrinting HardwareFilm RecordersOther Presentation ToolsFile StorageStorage MediaMagnetic RecordingDatabases for ImagesBrowsing and ThumbnailsLossless CodingReduced Color PalettesJPEG Image CompressionWavelet CompressionFractal CompressionDigital MoviesCORRECTING IMAGE DEFECTSContrast ExpansionNoisy ImagesNeighborhood AveragingNeighborhood RankingOther Neighborhood NoiseReduction MethodsDefect Removal, Maximum Entropy, and Maximum LikelihoodNonuniform IlluminationFitting a Background FunctionRank LevelingColor ImagesNonplanar ViewsComputer GraphicsGeometrical DistortionAlignmentInterpolationMorphingIMAGE ENHANCEMENT (PROCESSING IN THE SPATIAL DOMAIN)Contrast ManipulationHistogram EqualizationLaplacianDerivativesFinding EdgesRank OperationsTextureFractal AnalysisImplementation NotesImage MathSubtracting ImagesMultiplication and DivisionPrincipalComponents AnalysisOther Image CombinationsPROCESSING IMAGES IN FREQUENCY SPACEWhat Frequency Space is All AboutThe Fourier TransformFourier Transforms of Real FunctionsFrequencies and OrientationsPreferred OrientationTexture and FractalsIsolating Periodic NoiseSelective Masks and FiltersSelection of Periodic InformationConvolutionDeconvolutionNoise and Weiner DeconvolutionTemplate Matching and CorrelationAutocorrelationSEGMENTATION AND THRESHOLDINGThresholdingAutomatic SettingsMultiband Images TwoDimensional ThresholdsMultiband ThresholdingThresholding from TextureMultiple Thresholding CriteriaTextural OrientationRegion BoundariesSelective HistogramsBoundary LinesContoursImage RepresentationOther Segmentation MethodsThe General Classification ProblemPROCESSING BINARY IMAGESBoolean OperationsCombining Boolean OperationsMasksFrom Pixels to FeaturesBoolean Logic with FeaturesSelecting Features by LocationDouble ThresholdingErosion and DilationOpening and ClosingIsotropyMeasurements Using Erosion and DilationExtension to.
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(source: Nielsen Book Data) 9780849372544 20160528
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TA1637 .R87 2007  Unknown 
 Bellingham, Wash. : SPIE Press, c2007.
 Description
 Book — xi, 270 p. : ill. (some col.) ; 26 cm.
 Summary

This monograph examines selected applications of the optical correlation approaches and techniques in diverse problems of modern optics. These problems include linear singular optics of monochromatic, fully spatially coherent light fields; phase singularities in polychromatic (whitelight) optical fields; optical correlation techniques for diagnostics of rough surfaces; and Muellermatrix images of biological tissues and their statistical and fractal structures.
(source: Nielsen Book Data) 9780819465344 20160528
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TA1630 .O68 2007  Unknown 