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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

Questions

  1. State true or false: The use of the d_out[i]++ line instead of the atomicadd operation will yield an accurate result in histogram calculation.
  2. What is the advantage of using shared memory with atomic operations?
  3. What is the modification in the kernel call function when shared memory is used in the kernel?
  4. Which information can be obtained by calculating the histogram of an image?
  5. State true or false: The kernel function developed in this chapter for BGR into grayscale conversion will also work for RGB into grayscale conversion.
  6. Why is the image flattened in all of the examples shown in this chapter? Is it a compulsory step?
  7. Why is the image converted into the uint8 data type from the numpy library before being displayed?
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