A comprehensive reference book that addresses the field of machine vision and its significance in cyber-physical systems. It explores the multidisciplinary nature of machine vision, involving electronic and mechatronic devices, Artificial Intelligence algorithms, embedded systems, control systems, robotics, interconnectivity, Data Science, and cloud computing. The book aims to provide advanced students, early career researchers, and established scholars with state-of-the-art knowledge and novel content related to the implementation of machine vision in engineering, scientific knowledge, and technological innovation. The chapters of the book delve into various topics and applications within the realm of machine vision. They cover areas such as camera and inertial measurement unit calibration, technical vision systems for human detection, design and evaluation of support systems using neural networks, UV sensing in contemporary applications, fiber Bragg grating arrays for medical diagnosis, color model creation for terrain recognition by robots, navigation systems for aircraft, object classification in infrared images, feature selection for vehiclenon-vehicle classification, visualization of sedimentation in extreme conditions, quality estimation of tea using machine vision, image dataset augmentation techniques, machine vision for astronomical images, etc. The use of current technology requires measuring essential attributes from objects, health data, dimensions of a surface and weather, to mention some. These are necessary to do breakthrough innovations in a wide range of fields. The Artificial Intelligence (AI) field is one of them. This field is aimed at the research for imitating human abilities, above all, how they learn. AI can be divided into two main branches such as Machine Learning and Deep Learning.