# Machine Vision--Moving from Industry 4.0 to Industry 5.0
## 2 Foundations
### Machine Vision  
Vision is considered one of the most important human senses; it helps people perceive everything around them differently. The value and importance of vision has always been invaluable. For this reason, researchers since the 1980s have been working on providing machines with this ability, to see and perceive their surroundings, creating a research field widely known as \"machine vision\". Machine vision systems can provide numerous automation options to variable manufacturing industries in a fast, efficient, and constant manner. Therefore, the term \"machine vision\" was directly linked to industry in general but more specifically to electrical engineering and robotics. Later, the term \"computer vision\" was introduced, but it was not long before both terms were merged into a common scientific field, that of AI, offering countless considerations for their use. Machine vision, being a scientific subfield of AI, specifically tries to reproduce the sense of vision for machines through algorithms, covering areas even wider than human vision, namely from gamma rays to radio waves [13]. This reproduction is carried out in computers or robots. With the help of image sensors, digital visual data are obtained in diverse modalities such as images, videos, multidimensional images, etc., and along with supporting technologies such as image processing algorithms and communications, machine vision systems are created [14]. Machine vision systems in industry mostly consist of numerous cameras, which are placed according to existing requirements on assembly lines for product observation and data collection. They also provide the ability to read labels and automatically sort products without human intervention. Humans may be prone to errors due to tiredness and lack of adequate expertise. Consistency of automations combined with reduced human participation results in decreased errors and greater accuracy of detection, identification, supervision, measurements, etc. According to [PERSON] et al. [15], one of the reasons why the need for machine vision is crucial is because machines, systems, and robots have the ability to observe, communicate, and work with more precision, providing more opportunities. The first milestone for machine vision was in the 1950s, where [PERSON] developed optical flow of two-dimension imaging towards statistical pattern recognition [16]. In the 1960s, [PERSON] was the first to formally study 3D machine vision in his PhD thesis at MIT [17]. In the 1970s, [PERSON] from MIT developed an approach for scene understanding through computer vision, marking the beginning of real-word object tracking and low-level vision tasks such as detection and segmentation [18]. The development of both smart cameras and optical character recognition systems, in the 1980s, triggered the wide use of machine vision in ever increasing applications, starting from industrial ones, aiming to read letters and numbers to facilitate shorting and other product-related tasks. In the 1990s, machine vision invaded the industrial environments; companies started to sell commercial machine vision systems, while further technological advances were made from that time onwards, in terms of sensor functions, control architectures, and automation. The latter, along with the decrease in acquisition costs for machine vision systems, making them accessible to everyone, offered enhanced possibilities for machine vision systems to further advance. Therefore, since the early 1990s, machine vision has been developing rapidly. It has been closely connected to the cognitive field of machine learning and vision algorithms performed in real-time, providing significant results in a multitude of critical applications. In the 1990s, thousands of factories worldwide included machine vision systems in their production lines to automate many of their operations. Over the years, both machine vision algorithms and systems have been playing a key role in increasing industries' financial analytics incrementally in every report or sales survey.