- The three options to increase the performance are as follows:
- Having faster clock speed
- More work per clock cycle by a single processor
- Many small processors that can work in parallel. This option is used by GPU to improve performance.
- True
- CPUs are designed to improve latency and GPUs are designed to improve Throughput.
- The car will take 4 hours to reach the destination but it can only accommodate 5 persons, while the bus that can accommodate 40 persons takes 6 hours to reach the destination. The bus can transport 6.66 persons per hour, while the car can transport 1.2 persons per hour. Thus, car has better latency, and bus has better throughput.
- Image is nothing but a two dimensional array. Most of the computer vision applications involve processing of these two-dimensional arrays. It involves similar operations on a large amount of data, which can be efficiently...
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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Overview of this book
Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications.
To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples.
Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python.
By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach.
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Current Title:
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
Table of Contents (15 chapters)
Preface
Free Chapter
Introducing CUDA and Getting Started with CUDA
Parallel Programming using CUDA C
Threads, Synchronization, and Memory
Advanced Concepts in CUDA
Getting Started with OpenCV with CUDA Support
Basic Computer Vision Operations Using OpenCV and CUDA
Object Detection and Tracking Using OpenCV and CUDA
Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1
Deploying Computer Vision Applications on Jetson TX1
Getting Started with PyCUDA
Working with PyCUDA
Basic Computer Vision Applications Using PyCUDA
Assessments
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