Название: Digital Image Processing with C++: Implementing Reference Algorithms with the CImg LibraryАвтор: David Tschumperle, Christophe Tilmant, Vincent Barra
Издательство: CRC Press
Год: 2023
Страниц: 309
Язык: английский
Формат: pdf (true)
Размер: 52.3 MB
Digital Image Processing with C++: Implementing Reference Algorithms with the CImg Library presents the theory of digital image processing and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language using the free and easy-to-use CImg library.
Chapters cover the field of digital image processing in a broad way and propose practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization.
Why do image processing in C++?
Among the plethora of existing programming languages, the C++ language has the following advantages:
• It is a multi-paradigm, well-established, and popular language. It is generally taught in universities and engineering schools offering computer science related courses. It therefore reaches a wide audience, who will be able to use it to write programs addressing a wide range of problems, in order to solve various tasks, both at “low-level” and “high-level”.
• C++ is a compiled language, which produces highly optimized binaries. In image processing, the data to be processed is often large: a standard resolution image has several million values to analyze, and it is therefore important to have programs that are fast enough to iterate on these values within a reasonable time, which is not always possible with interpreted languages. In Python, for example, most of the existing modules for image processing are implemented in C/C++, for speed issues (if you have already tested looping over all pixels of an image with a “pure” Python loop, you guess why!).
• The use of C++ templates eases the manipulation of generic image data, for example, when the pixel values of images you process have different numerical types (Boolean, integer, floating point, etc.).
Students or developers wishing to discover or specialize in this discipline and teachers and researchers hoping to quickly prototype new algorithms or develop courses will all find in this book material to discover image processing or deepen their knowledge in this field.
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