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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

Histogram calculation in PyCUDA

The histogram of an image conveys important information related to the contrast of an image, and it can also be used as an image feature for computer vision tasks. A histogram indicates the frequency of the occurrence of a particular pixel value. While calculating the histogram of an 8-bit image that is 256 x 256 in size, the 65,535-pixel values will work on arrays of intensity values from 0-255. If one thread is launched per pixel, then 65,535 threads will work on 256 memory locations of intensity values.

Consider a situation in which a large number of threads try to modify a small portion of memory. While calculating the histogram of an image, read-modify-write operations have to be performed for all memory locations. This operation is d_out[i] ++, where first d_out[i] is read from memory, then incremented, and then written back to the memory...

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