Book Image

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

By : Bhaumik Vaidya
Book Image

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

By: Bhaumik Vaidya

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.
Table of Contents (15 chapters)

Filtering operations on images

The methods described till this point worked on a single pixel intensity, and are called point processing methods. Sometimes it is helpful to look at the neighborhood of a pixel rather than only single pixel intensity. This are called neighborhood processing techniques. The neighborhood can be 3 x 3, 5 x 5, 7 x 7, and so on and are matrix-centered at a particular pixel. Image filtering is an important neighborhood processing technique.

Filtering is an important concept in signal processing where we reject a certain band of frequencies and allow a certain band of frequency to pass. How is frequency measured in images? If gray levels change slowly over a region, then it is a low-frequency region. If gray levels changes drastically, then it is a high-frequency region. Normally the background of an image is considered a low-frequency region and the...