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The Resource Logic-based nonlinear image processing, Stephen Marshall, (electronic book)

Logic-based nonlinear image processing, Stephen Marshall, (electronic book)

Label
Logic-based nonlinear image processing
Title
Logic-based nonlinear image processing
Statement of responsibility
Stephen Marshall
Creator
Contributor
Subject
Language
eng
Summary
This text provides insight into the design of optimal image processing operators for implementation directly into digital hardware. Starting with simple restoration examples and using the minimum of statistics, the book provides a design strategy for a wide range of image processing applications. The text is aimed principally at electronics engineers and computer scientists, but will also be of interest to anyone working with digital images
Member of
Additional physical form
Also available in print.
Cataloging source
CaBNvSL
http://library.link/vocab/creatorDate
1958-
http://library.link/vocab/creatorName
Marshall, Stephen
Dewey number
621.36/7
Illustrations
illustrations
Index
index present
LC call number
TA1637
LC item number
.M338 2007e
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Society of Photo-optical Instrumentation Engineers
Series statement
Tutorial texts in optical engineering
Series volume
TT72
http://library.link/vocab/subjectName
  • Image processing
  • Nonlinear theories
  • Digital filters (Mathematics)
Target audience
  • adult
  • specialized
Label
Logic-based nonlinear image processing, Stephen Marshall, (electronic book)
Instantiates
Publication
Note
  • "SPIE digital library."
  • Title from PDF t.p. (viewed on 8/23/09)
  • System requirements: Adobe Acrobat Reader
Bibliography note
Includes bibliographical references and index
Color
black and white
Contents
  • Chapter 1. Introduction. Chapter 2. What is a logic-based filter? 2.1. Error criterion -- 2.2. Filter constraints -- 2.3. Window constraint -- 2.4. Translation invariance -- 2.5. Filter windows -- 2.6. Filter design -- 2.7. Minimizing the MAE -- 2.8. Summary -- References
  • Chapter 3. How accurate is the logic-based filter? 3.1. Optimum filter error -- 3.2. Other applications -- 3.3. Summary -- References
  • Chapter 4. How do you train the filter for a task? 4.1. Effect of window size -- 4.2. Training errors -- 4.3. In defense of training set approaches -- 4.4. Summary -- References
  • Chapter 5. Increasing filters and mathematical morphology. 5.1. Constraints on the filter function -- 5.2. Statistical relevance -- 5.3. Summary -- References
  • Chapter 6. The median filter and its variants. 6.1. The grayscale median as a special case of a generalized WOS filter -- 6.2. Binary WOS filters -- 6.3. Positive and negative medians -- 6.4. Weighted median filters -- 6.5. Optimum design of weighted rank and median filters -- 6.6. Weight-monotonic property -- 6.7. Design of weighted median filters -- 6.8. Summary -- References
  • Chapter 7. Extension to grayscale. 7.1. Stack filters -- 7.2. Grayscale morphology -- 7.3. Computational morphology for beginners -- 7.4. Elemental erosion -- 7.5. Aperture filters -- 7.6. Grayscale applications -- 7.7. Summary -- References
  • Chapter 8. Grayscale implementation. 8.1. Grayscale training issues -- 8.2. Hardware implementation -- 8.3. Stack filter -- 8.4. Grayscale morphology -- 8.5. Computational morphology and aperture filters -- 8.6. Efficient architecture for computational morphology and aperture filters -- 8.7. Summary -- References
  • Chapter 9. Case study: noise removal from astronomical images. 9.1. CCD noise in astronomical and solar images -- 9.2. Soft morphological filters -- 9.3. Results -- 9.4. Hardware implementation -- 9.5. Summary -- References
  • Chapter 10. Conclusions -- Reference
Dimensions
unknown
Extent
1 online resource (xiii, 147 p. : ill.)
File format
multiple file formats
Form of item
electronic
Isbn
9780819479006
Other physical details
digital file.
Reformatting quality
access
Reproduction note
Electronic resource.
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader
Label
Logic-based nonlinear image processing, Stephen Marshall, (electronic book)
Publication
Note
  • "SPIE digital library."
  • Title from PDF t.p. (viewed on 8/23/09)
  • System requirements: Adobe Acrobat Reader
Bibliography note
Includes bibliographical references and index
Color
black and white
Contents
  • Chapter 1. Introduction. Chapter 2. What is a logic-based filter? 2.1. Error criterion -- 2.2. Filter constraints -- 2.3. Window constraint -- 2.4. Translation invariance -- 2.5. Filter windows -- 2.6. Filter design -- 2.7. Minimizing the MAE -- 2.8. Summary -- References
  • Chapter 3. How accurate is the logic-based filter? 3.1. Optimum filter error -- 3.2. Other applications -- 3.3. Summary -- References
  • Chapter 4. How do you train the filter for a task? 4.1. Effect of window size -- 4.2. Training errors -- 4.3. In defense of training set approaches -- 4.4. Summary -- References
  • Chapter 5. Increasing filters and mathematical morphology. 5.1. Constraints on the filter function -- 5.2. Statistical relevance -- 5.3. Summary -- References
  • Chapter 6. The median filter and its variants. 6.1. The grayscale median as a special case of a generalized WOS filter -- 6.2. Binary WOS filters -- 6.3. Positive and negative medians -- 6.4. Weighted median filters -- 6.5. Optimum design of weighted rank and median filters -- 6.6. Weight-monotonic property -- 6.7. Design of weighted median filters -- 6.8. Summary -- References
  • Chapter 7. Extension to grayscale. 7.1. Stack filters -- 7.2. Grayscale morphology -- 7.3. Computational morphology for beginners -- 7.4. Elemental erosion -- 7.5. Aperture filters -- 7.6. Grayscale applications -- 7.7. Summary -- References
  • Chapter 8. Grayscale implementation. 8.1. Grayscale training issues -- 8.2. Hardware implementation -- 8.3. Stack filter -- 8.4. Grayscale morphology -- 8.5. Computational morphology and aperture filters -- 8.6. Efficient architecture for computational morphology and aperture filters -- 8.7. Summary -- References
  • Chapter 9. Case study: noise removal from astronomical images. 9.1. CCD noise in astronomical and solar images -- 9.2. Soft morphological filters -- 9.3. Results -- 9.4. Hardware implementation -- 9.5. Summary -- References
  • Chapter 10. Conclusions -- Reference
Dimensions
unknown
Extent
1 online resource (xiii, 147 p. : ill.)
File format
multiple file formats
Form of item
electronic
Isbn
9780819479006
Other physical details
digital file.
Reformatting quality
access
Reproduction note
Electronic resource.
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader

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