Externally indexed torrent

If you are the original uploader, contact staff to have it moved to your account

Textbook in PDF format

This comprehensive book is primarily intended for researchers, computer vision specialists, and high-performance computing specialists who are interested in parallelizing computer vision techniques for the sake of accelerating the run-time of computer vision methods. This book covers different penalization methods on different parallel architectures such as multi-core CPUs and GPUs. It is also a valuable reference resource for researchers at all levels (e.g., undergraduate and postgraduate) who are seeking real-life examples of speeding up the computer vision methods’ run-time.
Computer vision is one field that is considered compute-intensive. This is because the input can be an image or a video. As the input is of large size, then the computing/processing time is huge as well. In addition, deep learning methods are heavily used in the field of computer vision. Thus, utilizing the parallel architecture for improving the runtime of the computer vision methods is of great interest and benefit. Of note, the inputs of the computer vision methods are parallel-friendly. For instance, one image consists of a set of pixels. Those pixels can be divided into groups based on the number of available parallel resources and then each group of pixels is processed using one computational resource (e.g., CPU). Thus, the groups of pixels are processed in parallel. Similarly, the video input can be divided into frames, where each computational resource (e.g., CPU or GPU) handles a number of frames.
A Generic Multicore CPU Parallel Implementation for Fractional Order Digital Image Moments
Computer-Aided Road Inspection: Systems and Algorithms
Computer Stereo Vision for Autonomous Driving: Theory and Algorithms
A Survey on GPU-Based Visual Trackers
Accelerating the Process of Copy-Move Forgery Detection Using Multi-core CPUs Parallel Architecture
Parallel Image Processing Applications Using Raspberry Pi