Logarithmic Image Processing: Theory and Applications

 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 26. Juli 2016
  • |
  • 282 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
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978-0-12-805229-7 (ISBN)
 

Logarithmic Image Processing: Theory and Applications, the latest volume in the series that merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy and features cutting-edge articles on recent developments in all areas of microscopy, digital image processing, and many related subjects in electron physics.


  • Merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy into a single volume
  • Contains the latest information on logarithmic image processing and its theory and applications
  • Features cutting-edge articles on recent developments in all areas of microscopy, digital image processing, and many related subjects in electron physics
1076-5670
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 39,15 MB
978-0-12-805229-7 (9780128052297)
0128052295 (0128052295)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Logarithmic Image Processing: Theory and Applications
  • Copyright
  • Contents
  • Preface
  • Future Contributions
  • Acknowledgments
  • Foreword: Short History of the LIP Model
  • 1. Justification of the Model
  • 2. Origin of the Model
  • 2.1. Spirit and Goals of the Book
  • Main Notations
  • Chapter One: Gray-Level LIP Model. Notations, Recalls, and First Applications
  • 1. Basic Notations
  • 2. Definition of Logarithmic Laws on the Space of Images
  • 2.1. Definition of the Logarithmic Addition Law
  • 2.1.1. Fundamental Remarks
  • 2.2. Definition of the Logarithmic Scalar Multiplication
  • 2.3. Physical Interpretation of These Logarithmic Laws
  • 2.3.1. Addition Law
  • 2.3.2. Multiplication Law
  • 2.4. Consistency of the LIP Model with Human Vision
  • 3. First Consequences of the Vector Space Structure
  • 3.1. Logarithmic Interpolation
  • 3.1.1. First Application: Three-Dimensional Reconstruction of T4-Lymphocytes from Serial Cuts (See, for Example, Bron et ...
  • 3.1.2. Second Application: Dental Surgery
  • 3.2. Other ``Historical´´ Applications
  • 4. About a Good Use of the LIP Model
  • 4.1. Interest of the Isomorphism Between [0, M[ and R+
  • 4.2. Is It Possible to Define a Logarithmic Gray Scale?
  • 4.3. Is LIP Interest Limited to the Use of a Logarithmic Look-Up-Table?
  • 5. Transition to Next Chapters
  • Acknowledgment
  • References
  • Chapter Two: Various Contrast Concepts
  • 1. Logarithmic Additive Contrast
  • 1.1. Definition
  • 1.2. First Properties of the LAC
  • 1.3. Physical Meaning of the LAC
  • 1.4. Explicit Link Between Michelson´s Contrast and the Logarithmic Additive One
  • 1.5. Other Contrast Concepts in Link with the LAC
  • 1.6. Applications of the LAC
  • 1.6.1. Automated Thresholding and Multithresholding
  • 1.6.1.1. Notations and Recalls on Köhler´s Method
  • 1.6.1.2. New Method: LIP-Köhler Automated Thresholding
  • 1.6.1.3. Extension of Köhler´s Method to Multithresholding
  • 1.6.2. Contour Detection
  • 1.6.3. Region Growing
  • 2. Logarithmic Multiplicative Contrast
  • 2.1. Definitions, Notations
  • 2.2. Applications of the LMC
  • 2.2.1. Application to Automated Thresholding and Multithresholding
  • 2.2.2. Application to Contour Detection
  • 2.3. Another Contrast Concept in Link with the LMC
  • 3. What About Color Images?
  • 3.1. Logarithmic Image Processing for Color Images
  • 3.2. Extension of the LAC to Color Images
  • 3.3. Link with Mac-Adam Experiment
  • 4. Conclusion
  • References
  • Chapter Three: Metrics Based on Logarithmic Laws
  • 1. Introduction
  • 2. Recalls on Some Existing Metrics
  • 2.1. Definition of a Metric
  • 2.2. Examples of Functional Metrics
  • 2.2.1. The Most Classical
  • 2.2.2. Examples of Other Metrics
  • 2.2.2.1. Intermediate Metric Between ``Global´´ and ``Atomic´´
  • 2.2.2.2. A Bounded Metric Associating Binary and Gray-Level Approaches
  • 2.3. Examples of Metrics Defined on Binary Shapes
  • 3. Two Ways to Introduce Novel Metrics in the LIP Framework
  • 3.1. Extension of d1 and d in the LIP Sense
  • 3.2. Extension of the Intermediate Metric d1,SupR(f,g)
  • 3.3. Extension to Functions of the Binary Asplünd´s Metric
  • 4. The Multiplicative Asplünd´s Metric d?As
  • 4.1. Local Processing and Application to Target Tracking
  • 4.2. Neighborhoods Generated by the Multiplicative Asplünd´s Metric
  • 4.3. How to Overcome the Noise Sensitivity of Multiplicative Asplünd´s Metric
  • 5. The Additive Asplünd´s Metric d?As
  • 5.1. Definition of This Novel Metric
  • 5.2. Main Property: Insensitivity of the Additive Asplünd´s Metric to Exposure Time Variations
  • 5.2.1. Theoretical Case: Simulated Exposure Times
  • 5.2.2. Real Case
  • 5.3. Other Examples of Application
  • 5.3.1. Region Growing Algorithm
  • 5.3.1.1. Theoretical Case: Simulated Exposure Times
  • 5.3.1.2. Real Case
  • 5.3.2. Contour Detection
  • 5.4. Conclusion of Section 5 and Perspectives
  • 6. Examples of Metrics for Color Images
  • 6.1. LIP-C Approach
  • 6.2. Extension of LAC to Color Images and Associated Metrics
  • 6.3. Asplünd-Like Metrics for Color Images
  • 6.3.1. Multiplicative Asplünd-Like Metric
  • Between two colors
  • 6.3.2. Additive Asplünd-Like Metric
  • 7. Other Notions Around Metrics
  • 7.1. Recalls
  • 7.2. Notions Stronger than Metrics
  • 7.3. Notions Weaker than Metrics
  • 7.3.1. Recalls on Gauges Theory in Vector Spaces and Topological Vector Spaces
  • 7.3.2. Definition of Gauges in Image Processing
  • 8. Conclusion and Perspectives
  • References
  • Chapter Four: Dynamic Range Expansion, Night Vision. Stabilization, Centering. Industrial and Biomedical Applications
  • 1. A ``Killer´´ Property of the LIP Model: Negative Gray Levels and Negative Thickness of an Object May Be Interpreted as ...
  • 2. Low-Light Images Enhancement Through LIP Scalar Multiplication
  • 2.1. Remaining in the Gray Scale
  • 2.1.1. Dynamic Range Maximization
  • 2.1.2. Maximization of the Standard Deviation of the Histogram of ??f
  • 2.2. Full Dynamic Range Expansion Based on Negative Gray Levels
  • 2.3. What About Color Images?
  • 2.4. Conclusion of Section 2
  • 3. Low-Light Images Enhancement Based on LIP Subtraction
  • 3.1. Remaining in the Gray Scale
  • 3.1.1. Dynamic Range Maximization
  • 3.1.2. Maximization of the Standard Deviation of the Histogram of f?C
  • 3.2. Full Dynamic Range Expansion Based on Negative Gray Levels
  • 3.3. What About Color Images?
  • 4. Stabilization and Centering
  • 4.1. Assigning a Given Value to the Image Mean Gray Level
  • 5. Applications
  • 5.1. Confocal Microscopy
  • 5.2. Mosaïque Software (NT2I Company)
  • 5.3. Braille Alphabet
  • 5.4. Controlled Nanostructures Formation by Ultrafast Laser Pulses for Color Marking
  • 6. Local Lighting Correction
  • 7. Comparison of LIP Enhancement with Existing Methods
  • 7.1. Material and Method
  • 7.2. Results
  • 8. Conclusion and Perspectives
  • References
  • Chapter Five: Ability of the LIP Model to Simulate Variable Acquisition Conditions
  • 1. Lighting Variations: Source Intensity Variations
  • 2. Exposure Time Variation
  • 2.1. Gray-Level Images
  • 2.2. Color Images
  • 2.2.1. Simulation without Reference
  • 2.2.2. Quality Evaluation
  • 2.2.3. Acquisition of Moving Objects
  • 3. Variable Diaphragm Aperture
  • 4. Other Simulations
  • 4.1. Long Exposure Time
  • 4.2. Binning Simulation
  • 5. High Dynamic Range Images
  • 6. Conclusion
  • References
  • Chapter Six: Transfer of Classical Tools to the LIP Context
  • 1. Application to Segmentation Methods
  • 1.1. Introduction, Context, and Aim of the Section
  • 1.2. Multithresholding
  • 1.3. k-Means and ``Nuées Dynamiques´´
  • 1.4. Region Growing Algorithms
  • 1.4.1. Classical Region Growing
  • 1.4.2. LIP Version of Region Growing
  • 1.4.3. Systolic Region Growing
  • 1.5. Hierarchical Ascendant Classification
  • 1.5.1. Benzécri HAC
  • 1.5.1.1. Hierarchy
  • 1.5.1.2. Ultrametrics
  • 1.5.2. Benzécri HAC Application
  • 1.5.2.1. Bidimensional Images
  • 1.5.2.2. Benzécri Algorithm
  • 1.5.2.3. Bidimensional Algorithm
  • 1.5.2.4. Our Specific Approach
  • 1.5.2.5. HAC Color Version
  • 1.5.2.6. Results and Examples
  • 1.5.2.7. Section Conclusion
  • 1.6. Gravitational Clustering (Classical and LIP)
  • 1.6.1. Principle and Notations
  • 1.6.2. Adaptation to Image Processing
  • 1.6.3. Implementation
  • 1.6.3.1. Initialization of the Algorithm
  • 1.6.3.2. Heart of the Algorithm
  • 1.6.3.3. End of the Algorithm
  • 1.6.4. Metrics Selection
  • 1.6.5. Results
  • 1.6.6. Conclusion and Perspectives of this Section
  • 2. Logarithmic Wavelets
  • 2.1. Logarithmic Wavelet Denoising
  • 2.2. Denoising Evaluation
  • 3. Percolation
  • 3.1. Application 1: A New Approach of Textures Classification
  • 3.1.1. Computation of Percolation Trajectories
  • 3.1.2. Percolation Trajectories and Parameters Extraction
  • 3.1.3. Results
  • 3.1.4. A Variant of Percolation Method: Percolation by Propagation Front
  • 3.2. Application 2: Perception and Automated Detection of Polishing Scratches
  • 4. LIP Top-Hat
  • 5. Funnel-Shaped Growing in the Sense of Darsonville
  • 6. Application to Semitransparent Media Acquired in Reflection
  • 7. Conclusion
  • References
  • Chapter Seven: General Conclusion
  • Index
  • Contents of Volumes 151-194
  • Color Plate
  • Back Cover

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