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  • Book Overview & Buying Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

By : Vaidya
4.4 (5)
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

4.4 (5)
By: Vaidya

Overview of this book

Computer 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.
Table of Contents (15 chapters)
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Texture memory

Texture memory is another read-only memory that can accelerate the program and reduce memory bandwidth when data is read in a certain pattern. Like constant memory, it is also cached on a chip. This memory was originally designed for rendering graphics, but it can also be used for general purpose computing applications. It is very effective when applications have memory access that exhibits a great deal of spatial locality. The meaning of spatial locality is that each thread is likely to read from the nearby location what other nearby threads read. This is great in image processing applications where we work on 4-point connectivity and 8-point connectivity. A two-dimensional spatial locality for accessing memory location by threads may look something like this:

Thread 0 Thread 2
Thread 1

Thread 3

General global memory cache will not be able to capture...

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83
Tech Concepts
36
Programming languages
73
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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