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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 9

  1. The global memory for the GPU device on Jetson TX1 is around 4 GB with a GPU clock speed of around 1 GHz. This clock speed is slower than Geforce 940 GPU used earlier in this book. The memory clock speed is only 13 MHz compared to 2.505 GHz on Geforce 940, which makes Jetson TX1 slower. The L2 cache is 256 KB compared to 1 MB on Geforce 940. Most of the other properties are similar to GeForce 940.
  2. True
  3. In the latest Jetpack, OpenCV is not compiled with CUDA support nor does it have GStreamer support, which is needed for accessing the camera from the code. So, it is a good idea to remove OpenCV installation that comes with Jetpack and compile the new version of OpenCV with CUGA and GStreamer support.
  4. False. OpenCV can capture video from both USB and CSI camera connected to Jetson TX1 board.
  5. True. CSI camera is more close to hardware so frames are read quickly than USB...
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