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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 6

  1. OpenCV function to print pixel intensity at location(200,200) of any color image on the console is as follows:
cv::Mat h_img2 = cv::imread("images/autumn.tif",1);
cv::Vec3b intensity1 = h_img1.at<cv::Vec3b>(cv::Point(200, 200));
std::cout<<"Pixel Intensity of color Image at (200,200) is:" << intensity1 << std::endl;
  1. OpenCV function to resize an image to (300,200) pixels using bilinear Interpolation method is as follows:
cv::cuda::resize(d_img1,d_result1,cv::Size(300, 200), cv::INTER_LINEAR);

  1. OpenCV function to upsample an image by 2 using AREA interpolation is as follows:
int width= d_img1.cols;
int height = d_img1.size().height;
cv::cuda::resize(d_img1,d_result2,cv::Size(2*width, 2*height), cv::INTER_AREA);
  1. False. Blurring increases as we increase the size of a filter.
  2. False. The median filter can't remove Gaussian...
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