Computer Vision and Applications A Guide for Students and Practitioners 1st Edition by Bernd Jahne ISBN 0080502628 9780080502625

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Product details:

ISBN 10: 0080502628

ISBN 13: 9780080502625

Author:  Bernd Jahne 

Computer Vision and Applications: A Guide for Students and Practitioners 1st Edition: Based on the highly successful 3-volume reference Handbook of Computer Vision and Applications, this concise edition covers in a single volume the entire spectrum of computer vision ranging form the imaging process to high-end algorithms and applications. This book consists of three parts, including an application gallery.

• Bridges the gap between theory and practical applications
• Covers modern concepts in computer vision as well as modern developments in imaging sensor technology
• Presents a unique interdisciplinary approach covering different areas of modern science

 

Computer Vision and Applications: A Guide for Students and Practitioners 1st Edition Table of contents:

Chapter 1. Introduction

• Components of a vision system
• Imaging systems
• Signal processing for computer vision
• Pattern recognition for computer vision
• Performance evaluation of algorithms
• Classes of tasks
• References

Part I: Sensors and Imaging
Chapter 2. Radiation and Illumination

• Introduction
• Fundamentals of electromagnetic radiation
• Radiometric quantities
• Fundamental concepts of photometry
• Interaction of radiation with matter
• Illumination techniques
• References

Chapter 3. Imaging Optics

• Introduction
• Basic concepts of geometric optics
• Lenses
• Optical properties of glasses
• Aberrations
• Optical image formation
• Wave and Fourier optics
• References

Chapter 4. Radiometry of Imaging

• Introduction
• Observing surfaces
• Propagating radiance
• Radiance of imaging
• Detecting radiance
• Concluding summary
• References

Chapter 5. Solid-State Image Sensing

• Introduction
• Fundamentals of solid-state photosensing
• Photocurrent processing
• Transportation of photosignals
• Electronic signal detection
• Architectures of image sensors
• Color vision and color imaging
• Practical limitations of semiconductor photosensors
• Conclusions
• References

Chapter 6. Geometric Calibration of Digital Imaging Systems

• Introduction
• Calibration terminology
• Parameters influencing geometrical performance
• Optical systems model of image formation
• Camera models
• Calibration and orientation techniques
• Photogrammetric applications
• Summary
• References

Chapter 7. Three-Dimensional Imaging Techniques

• Introduction
• Characteristics of 3-D sensors
• Triangulation
• Time-of-flight (TOF) of modulated light
• Optical Interferometry (OF)
• Conclusion
• References

Part II: Signal Processing and Pattern Recognition
Chapter 8. Representation of Multidimensional Signals

• Introduction
• Continuous signals
• Discrete signals
• Relation between continuous and discrete signals
• Vector spaces and unitary transforms
• Continuous Fourier transform (FT)
• The discrete Fourier transform (DFT)
• Scale of signals
• Scale space and diffusion
• Multigrid representations
• References

Chapter 9. Neighborhood Operators

• Basics
• Linear shift-invariant filters
• Recursive filters
• Classes of nonlinear filters
• Local averaging
• Interpolation
• Edge detection
• Tensor representation of simple neighborhoods
• References

Chapter 10. Motion

• Introduction
• Basics: flow and correspondence
• Optical flow-based motion estimation
• Quadrature filter techniques
• Correlation and matching
• Modeling of flow fields
• References

Chapter 11. Three-Dimensional Imaging Algorithms

• Introduction
• Stereopsis
• Depth-from-focus
• References

Chapter 12. Design of Nonlinear Diffusion Filters

• Introduction
• Filter design
• Parameter selection
• Extensions
• Relations to variational image restoration
• Summary
• References

Chapter 13. Variational Adaptive Smoothing and Segmentation

• Introduction
• Processing of two- and three-dimensional images
• Processing of vector-valued images
• Processing of image sequences
• References

Chapter 14. Morphological Operators

• Introduction
• Preliminaries
• Basic morphological operators
• Advanced morphological operators
• References

Chapter 15. Probabilistic Modeling in Computer Vision

• Introduction
• Why probabilistic models?
• Object recognition as probabilistic modeling
• Model densities
• Practical issues
• Summary, conclusions, and discussion
• References

Chapter 16. Fuzzy Image Processing

• Introduction
• Fuzzy image understanding
• Fuzzy image processing systems
• Theoretical components of fuzzy image processing
• Selected application examples
• Conclusions
• References

Chapter 17. Neural Net Computing for Image Processing

• Introduction
• Multilayer perceptron (MLP)
• Self-organizing neural networks
• Radial-basis neural networks (RBNN)
• Transformation radial-basis networks (TRBNN)
• Hopfield neural networks
• Application examples of neural networks
• Concluding remarks
• References

Part III: Application Gallery

 

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