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)

Morphological operations on images

Image morphology deals with the regions and shapes of an image. It is used to extract image components that are useful to represent shapes and regions. Image morphology treats the image as an ensemble of sets unlike, other Image processing operations seen earlier. The image interacts with a small template, which is called a structuring element, and which defines the region of interest or neighborhood in the image morphology. There are various morphological operations that can be performed on images, which are explained one by one in this section:

  • Erosion: Erosion sets a center pixel to the minimum over all pixels in the neighborhood. The neighborhood is defined by the structuring element, which is a matrix of 1s and 0s. Erosion is used to enlarge holes in the object, shrink the boundary, eliminate the island, and get rid of narrow peninsulas...