Handbook of Computer Vision Algorithms in Image Algebra provides engineers, scientists, and students with an introduction to image algebra and presents detailed descriptions of over 80 fundamental computer vision techniques. These techniques represent a core of knowledge that all computer vision practitioners should have. The book also introduces the portable iac++ library, which supports image algebra programming in the C++ language. Image algebra provides a concise, high-level mathematical language with which one can specify computer vision and image processing transformations and algorithms. It is clear, lucid, and easy to learn and use. Because of their high level of abstraction, image algebra specifications can be directly implemented on a variety of computer architectures. Programs using the iac++ library can be executed efficiently on both sequential and parallel computer architectures without modification. To aid practitioners in formulating algorithms in succinct and precise mathematical language, computer vision methods are presented with clear mathematical specifications. To assist algorithm implementors in developing robust programs, the practical impact of algorithm variants and implementation choices is discussed.

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Handbook of Computer Vision Algorithms in Image Algebra provides engineers, scientists, and students with an introduction to image algebra and presents detailed descriptions of over 80 fundamental computer vision techniques. These techniques represent a core of knowledge that all computer vision practitioners should have. The book also introduces the portable iac++ library, which supports image algebra programming in the C++ language.
Image algebra provides a concise, high-level mathematical language with which one can specify computer vision and image processing transformations and algorithms. It is clear, lucid, and easy to learn and use. Because of their high level of abstraction, image algebra specifications can be directly implemented on a variety of computer architectures. Programs using the iac++ library can be executed efficiently on both sequential and parallel computer architectures without modification. To aid practitioners in formulating algorithms in succinct and precise mathematical language, computer vision methods are presented with clear mathematical specifications. To assist algorithm implementors in developing robust programs, the practical impact of algorithm variants and implementation choices is discussed.

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