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Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > Parallel processing
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA - Effective techniques for processing complex image data in real time using GPUs (Paperback)
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA - Effective techniques for processing complex image data in real time using GPUs (Paperback)
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Discover how CUDA allows OpenCV to handle complex and rapidly
growing image data processing in computer and machine vision by
accessing the power of GPU Key Features Explore examples to
leverage the GPU processing power with OpenCV and CUDA Enhance the
performance of algorithms on embedded hardware platforms Discover
C++ and Python libraries for GPU acceleration Book
DescriptionComputer vision has been revolutionizing a wide range of
industries, and OpenCV is the most widely chosen tool for computer
vision with its ability to work in multiple programming languages.
Nowadays, in computer vision, there is a need to process large
images in real time, which is difficult to handle for OpenCV on its
own. This is where CUDA comes into the picture, allowing OpenCV to
leverage powerful NVDIA GPUs. This book provides a detailed
overview of integrating OpenCV with CUDA for practical
applications. To start with, you'll understand GPU programming with
CUDA, an essential aspect for computer vision developers who have
never worked with GPUs. You'll then move on to exploring OpenCV
acceleration with GPUs and CUDA by walking through some practical
examples. Once you have got to grips with the core concepts, you'll
familiarize yourself with deploying OpenCV applications on NVIDIA
Jetson TX1, which is popular for computer vision and deep learning
applications. The last chapters of the book explain PyCUDA, a
Python library that leverages the power of CUDA and GPUs for
accelerations and can be used by computer vision developers who use
OpenCV with Python. By the end of this book, you'll have enhanced
computer vision applications with the help of this book's hands-on
approach. What you will learn Understand how to access GPU device
properties and capabilities from CUDA programs Learn how to
accelerate searching and sorting algorithms Detect shapes such as
lines and circles in images Explore object tracking and detection
with algorithms Process videos using different video analysis
techniques in Jetson TX1 Access GPU device properties from the
PyCUDA program Understand how kernel execution works Who this book
is forThis book is a go-to guide for you if you are a developer
working with OpenCV and want to learn how to process more complex
image data by exploiting GPU processing. A thorough understanding
of computer vision concepts and programming languages such as C++
or Python is expected.
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