Chapter 10 – Computer Vision
01 Jan 1995-pp 315-354
TL;DR: In this paper, the authors provide an overview of computer vision and object recognition and measurements, and discuss in detail about abstracting information from digital images, and the nature of digital images.
Abstract: Publisher Summary
This chapter provides an overview of computer vision. The chapter begins with defining digital image as an array of numbers called greyscales associated with an image. Each of the cells in a digital image is called a picture element or pixel. Usually the greyscales are interpreted in terms of brightness: pixels with large numbers are bright, pixels with smaller numbers are darker. One approach to finding out what is in the image is to reduce the complexity of 256 different grey levels to just two, i.e. to make the image binary. This simplification to black-and-white is known as binarization. One way to understand how to abstract information from a given class of digital images is for the vision engineer to study displays of them on a monitor. Although binarization and greyscale techniques can be used to create displays of digital images, in computer vision one is not primarily interested in creating pictures. The task is to abstract usable information from digital pictures. In practice this usually means going from the greyscale array to a string of symbols. This chapter discusses in detail about abstracting information from digital images, and the nature of digital images. Differences between computer vision and computer graphics are detailed. The chapter also elaborates about object recognition and measurements.
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11 Feb 1984
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
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