Learn Machine Vision with Snyder and Qi: A Practical Guide with Exercises and Software (PDF)

Machine Vision: A Comprehensive Introduction by Snyder and Qi

Machine vision is the field of computer science that deals with the analysis and understanding of images and videos. It is a fascinating and challenging subject that has many applications in various domains, such as robotics, biometrics, medical imaging, security, surveillance, entertainment, etc. Machine vision is also closely related to other disciplines, such as artificial intelligence, machine learning, pattern recognition, signal processing, etc.

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However, machine vision is not an easy subject to learn and master. It requires a solid background in mathematics, physics, and programming, as well as a good intuition and creativity for solving complex problems. Moreover, machine vision is a rapidly evolving field that constantly incorporates new theories, methods, and technologies.

Therefore, it is essential to have a comprehensive and up-to-date textbook that covers the fundamentals and the state-of-the-art of machine vision. This is exactly what this book by Snyder and Qi offers. This book is an accessible and comprehensive introduction to machine vision that provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. It also includes many programming exercises that give insights into the development of practical image processing algorithms.

This book is aimed at graduate students in electrical engineering, computer science, and mathematics, as well as researchers and practitioners who want to learn more about machine vision. It can also be used as a reference book for advanced undergraduate courses on image processing or computer vision. The book assumes some basic knowledge of calculus, linear algebra, probability, and programming, but it reviews these topics in the context of machine vision in the first part of the book.

The book is organized into four parts: Preliminaries, Preprocessing, Image Understanding, and The 2D Image in a 3D World. Each part consists of several chapters that cover different topics and aspects of machine vision. The book also has three appendices that provide additional information on support vector machines, differentiation of kernel operators, and the Image File System (IFS) software that is used throughout the book for image processing. The book also has an author index and a subject index for easy reference.

In this article, we will briefly summarize the main contents and features of each part and chapter of the book.

Part I: Preliminaries

This part introduces some basic concepts and tools that are needed for machine vision. It consists of four chapters:

Chapter 1: Computer Vision, Some Definitions, and Some History

This chapter defines what computer vision is and how it differs from image processing. It also gives a brief overview of the history and evolution of computer vision from its origins in the 1960s to its current status as a mature and diverse field. It also introduces some of the main subfields and tasks of computer vision, such as object recognition, face detection, optical character recognition, etc.

Chapter 2: Writing Programs to Process Images

This chapter explains how images are represented and manipulated by computers. It also introduces the Image File System (IFS) software that is used throughout the book for image processing. The IFS software is a collection of functions and commands that can perform various operations on images, such as reading, writing, displaying, converting, cropping, resizing, rotating, etc. The chapter also gives some examples of image processing programs using IFS, such as histogram equalization, image enhancement, image subtraction, etc.

Chapter 3: Review of Mathematical Principles

This chapter reviews some essential concepts and tools of linear algebra, calculus, and probability that are used in machine vision. It covers topics such as matrices, vectors, linear systems, eigenvalues, eigenvectors, derivatives, integrals, Taylor series, Fourier series, random variables, expectation, variance, covariance, correlation, etc. It also introduces the notation and terminology that are used in this book. The chapter also provides some exercises and problems to test the understanding of the mathematics.

Chapter 4: Images: Representation and Creation

This chapter discusses the physical properties of light and color, and how they affect the perception and representation of images. It also describes the methods and devices for capturing and displaying images, such as cameras, scanners, monitors, printers, etc. It also explains the formats and standards for storing and transmitting images, such as JPEG, PNG, BMP, TIFF, etc.

Part II: Preprocessing

This part covers some common techniques and algorithms for preprocessing images before applying higher-level machine vision tasks. It consists of three chapters:

Chapter 5: Kernel Operators

This chapter defines and analyzes kernel operators, which are one of the most fundamental and versatile tools for image processing. A kernel operator is a function that takes an image as input and produces another image as output by applying a local operation to each pixel of the input image. The chapter explains the properties and types of kernel operators, such as convolution, correlation, filtering, edge detection, etc. It also shows how to implement and optimize kernel operators using IFS.

Chapter 6: Noise Removal

This chapter deals with the problem of noise removal, which is the process of enhancing the quality of an image by reducing or eliminating unwanted variations or disturbances in the image. The chapter introduces the sources and models of noise in images, such as Gaussian noise, salt-and-pepper noise, speckle noise, etc. It also presents the criteria and methods for measuring noise and image quality, such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), etc. It also describes the techniques and algorithms for noise removal, such as smoothing, median filtering, adaptive filtering, wavelet denoising, etc.

Chapter 7: Mathematical Morphology

This chapter introduces mathematical morphology, which is a branch of image processing that uses set theory and logic to analyze and manipulate the shape and structure of images. The chapter explains the basic concepts and operations of mathematical morphology, such as dilation, erosion, opening, closing, etc. It also demonstrates the applications of mathematical morphology in image processing, such as segmentation, thinning, skeletonization, etc. It also explores the extensions and variations of mathematical morphology, such as grayscale morphology, morphological gradients, etc.

Part III: Image Understanding

This part focuses on some high-level machine vision tasks that involve analyzing and interpreting the content and meaning of images. It consists of four chapters:

Chapter 8: Segmentation

This chapter addresses segmentation, which is the process of dividing an image into meaningful regions or objects based on some criteria or features. The chapter defines and illustrates the goal of segmentation, and how it can facilitate subsequent machine vision tasks. It also presents the criteria and methods for evaluating segmentation results, such as accuracy, precision, recall, F-measure, etc. It also describes the techniques and algorithms for segmentation, such as thresholding, region growing, clustering, edge-based segmentation, etc.

Chapter 9: Parametric Transforms

This chapter introduces parametric transforms, which are functions that map an image to another domain or representation that can reveal some useful information or properties about the image. The chapter explains the concept and motivation of parametric transforms, and how they can simplify or enhance some machine vision tasks. It also discusses the types and examples of parametric transforms, such as Hough transform, Radon transform, Fourier transform, etc. It also shows the applications of parametric transforms in image processing, such as line detection, shape recognition, frequency analysis, etc.

Chapter 10: Representing and Matching Shape

This chapter explores shape representation and matching, which are two closely related problems that involve describing and comparing the shape or contour of an object or region in an image. The chapter discusses the challenges and approaches for shape representation and matching, and how they depend on factors such as scale, rotation, translation, deformation, occlusion, etc. It also presents the methods and examples of shape representation, such as boundary-based representation, region-based representation moment-based representation etc. It also presents the methods and examples of shape matching such as template matching distance-based matching feature-based matching etc.

Chapter 11: Representing and Matching Scenes

This chapter investigates scene representation and matching, which are two more complex problems that involve describing and comparing the whole scene or context of an image, rather than just individual objects or regions. The chapter explains the challenges and approaches for scene representation and matching, and how they depend on factors such as perspective, illumination, clutter, background, etc. It also introduces the methods and examples of scene representation, such as hierarchical representation, graph-based representation, semantic representation, etc. It also introduces the methods and examples of scene matching, such as alignment-based matching, correspondence-based matching, inference-based matching, etc.

Part IV: The 2D Image in a 3D World

This part deals with some advanced machine vision tasks that involve relating 2D images to 3D scenes or objects. It consists of two chapters:

Chapter 12: Relating to Three Dimensions

This chapter discusses the problems and principles of relating 2D images to 3D scenes or objects, and how to recover depth information from images. The chapter introduces the concepts and models of perspective projection, camera calibration, stereo vision, etc. It also explains the techniques and algorithms for recovering depth information from images, such as triangulation, structure from motion, shape from shading, etc.

Chapter 13: Developing Computer Vision Algorithms

This chapter provides some practical guidance and advice for developing computer vision algorithms. It outlines the steps and guidelines for developing computer vision algorithms, such as problem definition, literature review, algorithm design, implementation, testing, evaluation, etc. It also suggests some tools and resources for testing and evaluating computer vision algorithms, such as datasets, benchmarks, metrics, software libraries, etc. It also presents some case studies of computer vision algorithms in action, such as face detection, optical character recognition, object tracking, etc.

Appendix A: Support Vector Machines

This appendix introduces support vector machines (SVMs), which are a powerful and popular machine learning technique that can be used for classification and regression tasks. The appendix explains the introduction and motivation of SVMs, the theory and formulation of SVMs, and the applications and examples of SVMs in computer vision.

Appendix B: How to Differentiate a Function Containing a Kernel Operator

This appendix derives and explains the differentiation rule for kernel operators, which are widely used in image processing and machine vision. The appendix shows how to differentiate a function that contains a kernel operator with respect to its input image or its kernel function. It also gives some examples and exercises of applying the differentiation rule.

Appendix C: The Image File System (IFS) Software

This appendix describes and installs the Image File System (IFS) software that is used throughout the book for image processing. The IFS software is a collection of functions and commands that can perform various operations on images, such as reading, writing, displaying, converting, cropping, resizing, rotating, etc. The appendix provides the documentation and usage of the IFS functions and commands. It also provides the source code and license of the IFS software.

Author Index

This index lists the names of the authors who are cited or referenced in the book.

Subject Index

This index lists the terms and topics that are discussed or mentioned in the book.

Conclusion

In this article, we have summarized the main contents and features of the book Machine Vision: A Comprehensive Introduction by Snyder and Qi. This book is an accessible and comprehensive introduction to machine vision that provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. It also includes many programming exercises that give insights into the development of practical image processing algorithms. This book is suitable for graduate students in electrical engineering, computer science, and mathematics, as well as researchers and practitioners who want to learn more about machine vision. It can also be used as a reference book for advanced undergraduate courses on image processing or computer vision.

FAQs

Here are some frequently asked questions about this book:

• What are the prerequisites for reading this book?

The book assumes some basic knowledge of calculus, linear algebra, probability, and programming, but it reviews these topics in the context of machine vision in the first part of the book.

• What software is used in this book for image processing?

The book uses the Image File System (IFS) software that is provided in Appendix C for image processing. The IFS software is a collection of functions and commands that can perform various operations on images, such as reading, writing, displaying, converting, cropping, resizing, rotating, etc.

• What are the main features and benefits of this book?

The book has the following features and benefits:

• It provides a comprehensive and up-to-date coverage of the fundamentals and the state-of-the-art of machine vision.

• It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems.

• It includes many programming exercises that give insights into the development of practical image processing algorithms.

• It uses a clear and consistent notation and terminology throughout the book.

• It uses a pedagogical and engaging style that makes the subject accessible and interesting.

• How can I get a copy of this book?

You can get a copy of this book from the publisher's website or from online retailers such as Amazon. You can also access the online version of this book from the publisher's website or from Cambridge Core.

• How can I contact the authors of this book?

You can contact the authors of this book by email or by visiting their websites. Their contact information is as follows:

• Wesley E. Snyder: wes@ncsu.edu, https://www.ece.ncsu.edu/people/wes/

• Hairong Qi: hqi@utk.edu, https://www.eecs.utk.edu/people/hairong-qi/

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