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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 10

  1. Python is Open Source and has a large user community contributing to the language in terms of modules. These modules can be used easily to develop applications in a small time with few lines of code. The syntax of Python language is easy to read and interpret, which makes it easier to learn for a new programmer. It is an interpreted language that allows line by line execution of the code. These are the few advantages of python over C/C++.
  2. The whole code is checked and converted to machine code in compiled type languages, while one statement at a time is translated in an interpreted language. An interpreted language requires less amount of time to analyze the source code, but the overall execution time is slower compared to compile type languages. Interpreted languages do not generate intermediate code as in the case of compiled type languages.
  3. False. Python is an interpreted...
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