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Название: Recent Advances in Computer Vision Applications Using Parallel Processing
Автор: Khalid M. Hosny, Ahmad Salah
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 126
Язык: английский
Формат: pdf (true), epub
Размер: 26.5 MB
This comprehensive book is primarily intended for researchers, computer vision specialists, and high-performance computing specialists who are interested in parallelizing computer vision techniques for the sake of accelerating the run-time of computer vision methods. This book covers different penalization methods on different parallel architectures such as multi-core CPUs and GPUs. It is also a valuable reference resource for researchers at all levels (e.g., undergraduate and postgraduate) who are seeking real-life examples of speeding up the computer vision methods’ run-time. Computer vision is one field that is considered compute-intensive. This is because the input can be an image or a video. As the input is of large size, then the computing/processing time is huge as well. In addition, Deep Learning methods are heavily used in the field of computer vision. Thus, utilizing the parallel architecture for improving the runtime of the computer vision methods is of great interest and benefit.
Автор: Khalid M. Hosny, Ahmad Salah
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 126
Язык: английский
Формат: pdf (true), epub
Размер: 26.5 MB
This comprehensive book is primarily intended for researchers, computer vision specialists, and high-performance computing specialists who are interested in parallelizing computer vision techniques for the sake of accelerating the run-time of computer vision methods. This book covers different penalization methods on different parallel architectures such as multi-core CPUs and GPUs. It is also a valuable reference resource for researchers at all levels (e.g., undergraduate and postgraduate) who are seeking real-life examples of speeding up the computer vision methods’ run-time. Computer vision is one field that is considered compute-intensive. This is because the input can be an image or a video. As the input is of large size, then the computing/processing time is huge as well. In addition, Deep Learning methods are heavily used in the field of computer vision. Thus, utilizing the parallel architecture for improving the runtime of the computer vision methods is of great interest and benefit.