Avimanyu Bandyopadhyay - Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems

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Bhaumik Vaidya is an experienced computer vision engineer and mentor. He has worked extensively on OpenCV Library in solving computer vision problems. He is a University gold medalist in masters and is now doing a PhD in the acceleration of computer vision algorithms built using OpenCV and deep learning libraries on GPUs. He has a background in teaching and has guided many projects in computer vision and VLSI( Very-large-scale integration) . He has worked in the VLSI domain previously as an ASIC verification engineer, so he has very good knowledge of hardware architectures also. He has published many research papers in reputable journals to his credit. He, along with his PhD mentor, has also received an NVIDIA Jetson TX1 embedded development platform as a research grant from NVIDIA.

Vandana Shah gained her bachelor's degree in electronics in the year 2001. She has also gained an MBA in Human Resource management an a master's in Electronics Engineering specifically in the VLSI domain. She has also submitted her thesis for a PhD in electronics, specifically concerning the domain of image processing and deep learning for brain tumor detection, and is awaiting her award. Her area of interest is image processing with deep learning and embedded systems. She has more than 13 years of experience in research, as well as in teaching and guiding undergraduate and postgraduate students of electronics and communications. She has published many papers in renowned journals, such as IEEE, Springer, and Inderscience. She is also receiving a government grant for her upcoming research in the MRI image-processing domain. She has dedicated her life to mentoring students and researchers. She is also able to train students and faculty members in soft-skill development. Besides her prowess in technical fields, she also has a strong command of Kathak, an Indian dance.

"I thank my family members for their full support."

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Computer vision is revolutionizing a wide range of industries and OpenCV is the most widely chosen tool for computer vision with the ability to work in multiple programming languages. Nowadays, there is a need to process large images in real time in computer vision which is difficult to handle for OpenCV on its own. In this Graphics Processing Unit (GPU) and CUDA can help. So this book provides a detailed overview on integrating OpenCV with CUDA for practical applications. It starts with explaining the programming of GPU with CUDA which is essential for computer vision developers who have never worked with GPU. Then it explains OpenCV acceleration with GPU and CUDA by taking some practical examples. When computer vision applications are to be used in real life scenarios then it needs to deployed on embedded development boards. This book covers the deployment of OpenCV applications on NVIDIA Jetson Tx1 which is very popular for computer vision and deep learning applications. The last part of the book covers the concept of PyCUDA which can be used by Computer vision developers who are using OpenCV with Python. PyCUDA is a python library which leverages the power of CUDA and GPU for accelerations. This book provides a complete guide for developers using OpenCV in C++ or Python in accelerating their computer vision applications by taking a hands on approach.