Logic-based nonlinear image processing
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. Tr…
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- Full title :Logic-based nonlinear image processing / Stephen Marshall.
Summary
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.
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.
Edition Statements
- Publication Date:©2007.
Publication Statements
- Publisher(s):SPIE
- Place of Publication:Bellingham, Wash. <1000 20th St. Bellingham WA 98225-6705 USA>
- Language:eng
- Publication Date:©2007.
Notes
"SPIE digital library. - System requirements: Adobe Acrobat Reader. - Includes bibliographical references and index. - Title from PDF title page (viewed on 8/23/09).
The Physical Object
- Description:1 online resource (xiii, 147 Seiten : : Illustrationen)
Edition Identifiers
- ISBN:9780819479006, 0819479004
- oclc:ocn812342498
- Dewey Decimal:621.367
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