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Computer and Machine Vision

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EBOOK
Descripcion
Indice
Autores
Muestra
Ficha Técnica
Descripción

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject.

Key features include:

  • Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice
  • New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision
  • Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
  • Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
  • The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject


  • Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
  • Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
  • The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject

1. Chapter 1. Introduction 2. Chapter 2. A Foundation for Robust Methods 3. Chapter 3. Estimating Measures of Location and Scale 4. Chapter 4. Confidence Intervals in the One-Sample Case 5. Chapter 5. Comparing Two Groups 6. Chapter 6. Some Multivariate Methods 7. Chapter 7. One-Way and Higher Designs for Independent Groups 8. Chapter 8. Comparing Multiple Dependent Groups 9. Chapter 9. Correlation and Tests of Independence 10. Chapter 10. Robust Regression 11. Chapter 11. More Regression Methods 12. References 13. Index

Descripcion
Descripción

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject.

Key features include:

  • Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice
  • New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision
  • Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
  • Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
  • The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject


  • Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
  • Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
  • The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject

1. Chapter 1. Introduction 2. Chapter 2. A Foundation for Robust Methods 3. Chapter 3. Estimating Measures of Location and Scale 4. Chapter 4. Confidence Intervals in the One-Sample Case 5. Chapter 5. Comparing Two Groups 6. Chapter 6. Some Multivariate Methods 7. Chapter 7. One-Way and Higher Designs for Independent Groups 8. Chapter 8. Comparing Multiple Dependent Groups 9. Chapter 9. Correlation and Tests of Independence 10. Chapter 10. Robust Regression 11. Chapter 11. More Regression Methods 12. References 13. Index

Indice
Autores
Muestra
Ficha Técnica