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

You're reading from   Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA Effective techniques for processing complex image data in real time using GPUs

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789348293
Length 380 pages
Edition 1st Edition
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Author (1):
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Bhaumik Vaidya Bhaumik Vaidya
Author Profile Icon Bhaumik Vaidya
Bhaumik Vaidya
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Table of Contents (15) Chapters Close

Preface 1. Introducing CUDA and Getting Started with CUDA 2. Parallel Programming using CUDA C FREE CHAPTER 3. Threads, Synchronization, and Memory 4. Advanced Concepts in CUDA 5. Getting Started with OpenCV with CUDA Support 6. Basic Computer Vision Operations Using OpenCV and CUDA 7. Object Detection and Tracking Using OpenCV and CUDA 8. Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1 9. Deploying Computer Vision Applications on Jetson TX1 10. Getting Started with PyCUDA 11. Working with PyCUDA 12. Basic Computer Vision Applications Using PyCUDA 13. Assessments 14. Other Books You May Enjoy

Chapter 5

  1. There is a difference between image processing and computer vision fields. Image processing is concerned with improving the visual quality of images by modifying pixel values, whereas computer vision is concerned with extracting important information from the images. So, in image processing, both input and output are images, while in computer vision, input is an image but the output is the information extracted from that image.
  2. The OpenCV library has an interface in C, C++, Java, and Python languages and it can be used in all operating systems like Windows, Linux, Mac, and Android without modifying the single line of code. This library can also take advantage of multi-core processing. It can take advantage of OpenGL and CUDA for parallel processing. As OpenCV is lightweight, it can be used on embedded platforms like Raspberry Pi as well. This makes it ideal for deploying...
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