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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 and equalization in OpenCV

A histogram is a very important property of an image as it provides a global description of the appearance of that image. An enormous amount of information can be obtained from the histogram. It represents the relative frequency of occurrence of gray levels in an image. It is basically a plot of gray levels on the X-axis and the number of pixels in each gray level on the Y-axis. If the histogram is concentrated on the left side then the image will be very dark and if it is concentrated on the right side, then the image will be very bright. It should be evenly distributed for good visual quality of an image.

The following image demonstrates histograms for dark, bright, and normal images:

OpenCV provides a function to calculate the histogram of an Image. The syntax of the function is as follows:

void cv::cuda::calcHist ( InputArray...
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