Название: Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, technologies and applications
Автор: Chiranji Lal Chowdhary, Mamoun Alazab
Издательство: The Institution of Engineering and Technology
Год: 2021
Страниц: 505
Язык: английский
Формат: pdf (true)
Размер: 36.8 MB

Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using Artificial Intelligence (AI) based on neural networks, Machine Learning and Deep Learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring.

Written by a team of international experts, this edited book covers the state-of-the-art of advanced research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real world applications including object detection, biometrics, Deepfake detection, sentiment and emotion analysis, traffic enforcement camera monitoring, vehicle control and aerial remote sensing imagery.

Computer vision, pattern recognition, Deep Learning (DL), expert systems, cognitive computing, and the Internet of things are some of the innovations and terminologies that have sprung up as Artificial Intelligence (AI) has grown in popularity. Among these, computer vision is one of the innovations that allow computers to perceive and comprehend the visual world. Computers recognize and classify artifacts using digital images and DL representations. Computer vision technologies have exploded in popularity in the fields of automation and logistics. Despite these challenges, automation appears to be one of the most exciting regions for recently developed artificial intelligence solutions, primarily computer and machine vision frameworks. Amongst the most important problems in automation is the protection of human–computer and human–machine interactions, which necessitates the “explainability” of techniques, which also precludes the use of any DL-based solutions, regardless of their success in computer vision applications.

The book will be useful for industry and academic researchers, scientists and engineers in the fields of computer vision, machine vision, image processing and recognition, multimedia, AI, machine and Deep Learning, Data Science, biometrics, security, and signal processing. It will also make a great course reference for advanced students and lecturers in these fields of research.