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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

Product type Book

Published in Sep 2018

Publisher Packt

ISBN-13 9781789348293

Pages 380 pages

Edition 1st Edition

Languages

Concepts

Computer Vision

Author (1):

Bhaumik Vaidya

Table of Contents (15) Chapters

Preface

1. Introducing CUDA and Getting Started with CUDA

2. Parallel Programming using CUDA C

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

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Questions

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.
What is the advantage of using shared memory with atomic operations?
What is the modification in the kernel call function when shared memory is used in the kernel?
Which information can be obtained by calculating the histogram of an image?
State true or false: The kernel function developed in this chapter for BGR into grayscale conversion will also work for RGB into grayscale conversion.
Why is the image flattened in all of the examples shown in this chapter? Is it a compulsory step?
Why is the image converted into the uint8 data type from the numpy library before being displayed?

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Authors (1)

Bhaumik Vaidya

Bhaumik Vaidya is an experienced computer vision engineer and mentor. He has worked extensively on OpenCV Library in solving computer vision problems. He is a University gold medalist in masters and is now doing a PhD in the acceleration of computer vision algorithms built using OpenCV and deep learning libraries on GPUs. He has a background in teaching and has guided many projects in computer vision and VLSI(Very-large-scale integration). He has worked in the VLSI domain previously as an ASIC verification engineer, so he has very good knowledge of hardware architectures also. He has published many research papers in reputable journals to his credit. He, along with his PhD mentor, has also received an NVIDIA Jetson TX1 embedded development platform as a research grant from NVIDIA.

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