Parallel Computer Vision
Published on 5. August 2014
318 pages
978-0-323-15620-2 (ISBN)
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Parallel Computer Vision
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Leonard Uhr
Parallel Computer Vision
Book
07/1987
Academic Press
€54.46
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Content
- Front Cover
- Parallel Computer Vision
- Copyright Page
- Table of Contents
- Contributors
- Preface
- Part I: Primarily Pyramid Architectures, and How They Can Be Used
- Chapter 1. PAPIA: A Case History
- 1. Parallelism for Image Processing
- 2. The National Project of Parallelism for Signal and Image Analysis
- 3. Machine Taxonomy
- 4. Benchmark Definition
- 5. The Pyramid Structure
- 6. Functional Simulation of the Pyramid Structure
- 7. A First Design of the PAPIA Chip
- 8. The Hardware Architecture
- 9. The Software Environment
- 10. Conclusions and Future Plans
- 11. References
- Chapter 2. The GAM Pyramid
- 1. Introduction
- 2. Pyramid Description
- 3. The GAM Pyramid System Software
- 4. Basic Image Understanding Algorithms
- 5. Object Identification
- 6. Concluding Remarks
- 7. Acknowledgements
- 8. References
- Chapter 3. A Prototype Pyramid Machine for Hierarchical Cellular Logic
- 1 Introduction
- 2 Pyramid-Machine Architecture
- 3 General Design
- 4 The HCL Chip: A Custom VLSI Circuit
- 5 Pyramid Controller
- 6 Hardware
- 7 Discussion
- 8 Acknowledgements
- 9 References
- Part II: Primarily Algorithms and Ways of Using and Expanding Pyramids
- Chapter 4. NETRA: An Architecture for a Large Scale Multiprocessor Vision System
- 1. Introduction
- 2. Motivation
- 3. Architectural Issues
- 4. The Organization of NETRA
- 5. Architectural Features
- 6. Conclusion
- References
- Chapter 5. The Scale-space Formulation of Pyramid Data Structures
- 0. Abstract
- 1. Scale-space
- 2. The Gaussian Pyramid
- 3. The Laplacian Pyramid
- 4. Zero-crossings
- 5. Some Results using Scale-space
- 6. Summary
- 7. Acknowledgements
- References
- Chapter 6. Custom-Made Pyramids
- 1. Abstract
- 2. Pyramids
- 3. One-Dimensional Resampling
- 4. Pyramid Construction
- 5. Adaptive Resampling
- 6. Irregular Tessellations
- 7. Summary
- 8. Acknowledgements
- References
- Chapter 7. Pyramid Algorithms Optimal for the Worst Case
- 1. Abstract
- 2. Introduction
- 3. Lower Bounds
- 4. Poly-log Algorithms for the Standard Pyramid
- 5. Algorithms for Pyramid-Bound Problems
- 6. Architectures for Perimeter Bound Problems
- 7. Conclusions
- 8. Acknowledgements
- 9. References
- Part III: Algorithms and Programs for Appropriately Augmented Architectures
- Chapter 8. Multiscale Image Understanding
- 1. Abstract
- 2. Introduction
- 3. Constructing Multiscale Representations
- 4. Analyzing Multiscale Representations
- 5. Multiscale Model-based Object Recognition
- 6. Multiscale Texture Segmentation
- 7. Acknowledgment
- 8. References
- Chapter 9. The UMass Image Understanding Architecture
- 1. Introduction
- 2. Knowledge-Based Vision
- 3. Parallel Architectures for Knowledge-Based Vision
- 4. The UMass Image Understanding Architecture (IUA)
- 5. The Operating Environment
- 6. The Development Environment
- 7. Further Development
- 8. Conclusion
- 9. Acknowledgments
- 10. References
- Chapter 10. Highly Parallel, Hierarchical, Recognition Cone Perceptual Structures
- 1. Abstract
- 2. Introduction
- 3. The Living Perceptual System
- 4. Parallel-Serial Hardware Architectures for Perception Systems
- 5. A Brief Look at some of the Ways Pyramids Can be Used for Perception
- 6. Recognition Cone Perception Programs, and Their Ultimate Goals
- 7. How Recognition Cones Can Be Used for Perception
- 8. Simple Examples of Actual Image-Driven Recognition Cones
- 9. Parallel Reasoning and Inference, to Increase Power and Flexibility
- 10. Recognizing and Describing Scenes of Several Objects
- 11. Handling Binocular, N-Ocular, and Multi-Modal Images
- 12. The Perception of Objects in Motion
- 13. Toward Software/Hardware Structures That Rival, Or/And Model, Living Visual Systems
- 14. Acknowledgements
- 15. References
- Index
