Computer vision is one of the most fascinating and complex artificial intelligence techniques, inspired from the outset by the idea of seeing the world as the human eye would, neurally connected to the biological brain.
If AI represents the brain, artificial vision can be associated with the activity of the eye and its fundamental functions, which enable us to see and understand real-world phenomena. In other words, computer vision allows artificial systems to observe and interpret the context being analysed, drawing inspiration from what a human would do in the same circumstances.
Computer vision is a complex discipline that involves interdisciplinary studies and research, including within the field of artificial intelligence itself, where machine learning, deep learning, convolutional neural networks (CNNs), and many other techniques come into play.
Through Moxoff’s experience, we explore what computer vision is, how it works, and the advantages that can currently be gained from its informed implementation in business processes.
What is Computer Vision
According to the definition provided by Gartner, computer vision is “a set of technologies that involves capturing, processing, and analysing real-world images and videos in order to extract relevant and contextual information from the physical world”.
Computer vision uses devices, data, and algorithms to analyse what the human eye would perceive, but in a much faster and more accurate way, seeking to provide a similar interpretation. For these reasons, computer vision is used to support human vision, especially in highly automated contexts where it is necessary to see where the biological eye cannot reach. For example, analysing processes, inspecting products or checking the functionality of production assets, even going so far as to detect anomalies that are imperceptible to humans.
Computer vision boasts applications in numerous fields and underpins functions that are now part of our daily lives, such as text recognition (OCR), facial recognition, or augmented reality itself.
The evolution of Computer Vision
The origins of artificial vision can be traced back to the 1950s, when some neurophysiologists began presenting images to animals in order to understand their brain reactions.
In the 1960s, the first digital scanning technologies were developed and research in the field of artificial intelligence began to address the problem of vision. During the following decade, the first OCR (optical character recognition) applications were created to recognise and digitise handwritten text, using neural networks.
In the 1980s, neuroscientist David Marr translated the hierarchical workings of vision into algorithms capable of automatically detecting the edges of an object and its basic shapes. In those years, the Neocognitron was developed, the first convolutional neural network (CNN), which paved the way for the next generation of computer vision.
After the so-called 'AI winter', the 2000s saw the emergence of applications we still use today, such as facial recognition, along with the definition of standard methods for visual data labelling.
In the 2010s, convolutional neural networks became increasingly efficient and accurate in their predictions, reducing the risk of error in image recognition to percentages acceptable even in production contexts.
How Computer Vision works
As previously mentioned, artificial vision makes use of digital data and artificial intelligence techniques, including machine learning and deep learning, to replicate the workings of human vision. The algorithms used in computer vision applications are trained using large datasets of visual information, learning how to classify, identify, and track objects within a given context.
Image analysis
The process of image analysis makes use of pattern recognition techniques. The acquired image is interpreted by comparing it with reference models known beforehand, for example, detecting abnormal conditions that may correspond to a quality defect in a product, an attempted fraud, or a possible early sign of pathology in radiological examinations.
Thanks to machine learning, pattern recognition systems process increasing volumes of images over time, progressively improving their level of accuracy and meeting application requirements more reliably.
Fundamental functions
The fundamental functions of a computer vision system are classification, identification and object tracking.
- Object classification (object classification): the system classifies the objects present in the image according to predefined categories, for example, identifying how many flowers are in an image depicting a landscape.
- Object identification (object identification): the system identifies the objects present in the image in order to distinguish their significant details, such as people's facial features.
- Object tracking (object tracking): the system analyses a video sequence to follow the position of a specific object over a timeline.
Artificial Intelligence
Within its interdisciplinary context, it is important to highlight that modern computer vision adopts a different approach compared to traditional statistical methods for image analysis, utilising deep learning techniques based on machine learning through neural networks. These are capable of extracting much more knowledge from images than traditional systems. One of the main advantages of machine learning lies in its ability to continually enhance the value of image analysis, using the outputs as part of the input for the next cycle, thereby improving the model’s predictive accuracy.
The advantages of Computer Vision
When implemented correctly and thoughtfully, computer vision enables companies to achieve significant advantages in every respect.
Improvement of operational efficiency
In synergy with other AI techniques, computer vision enables the automation of activities traditionally carried out manually, such as sorting and packaging in warehouses and distribution centres. This allows for faster processing times and reduces the need for low value-added human labour, which is replaced by robotic systems.
Cost reduction and productivity increase
Computer vision is able to automate many processes, making them more economically sustainable and increasing productivity levels as a result. Consider, for example, the ability to recognise text, which underpins document management or real-time translation applications, or the ability to recognise objects and people within social media contexts.
Greater safety and product quality
Computer vision makes it possible to improve safety in various ways; for example, consider its applications in urban video surveillance within smart cities. Such systems are capable of monitoring public spaces, detecting security issues, and automatically triggering emergency procedures. In the context of autonomous driving, computer vision enables, among other things, the recognition of obstacles along the route.
Computer vision is also used in the field of quality control in production, thanks to its ability to detect any anomalies compared to standard conditions. A high level of quality often results in greater customer satisfaction and an enhanced brand reputation.
The challenges of Computer Vision
As a relatively recent technology, computer vision inevitably brings with it some new challenges, which will need to be addressed with appropriate solutions over time.
Privacy issues
Images captured by computer vision systems that depict individuals’ identities are inevitably subject to personal data regulations and considerations regarding fundamental human rights, as required, within European jurisdictions, by the recent AI Act, which classifies many applications as “high risk”.
The storage and processing of acquired data are also governed by the GDPR, and sector-specific regulations—such as those in healthcare and finance—may also be relevant.
One of the main issues is related to deepfake technologies, which make it easy to replace the identities of real people, impersonating them for fraudulent purposes, blackmail, and other forms of cybercrime.
Technical limitations and accuracy
Although modern AI systems are becoming increasingly reliable, the possibility of error cannot be excluded, such as errors resulting from the analysis of low-quality data or data that is not well suited to the context being analysed. Therefore, the general considerations regarding artificial intelligence also apply to computer vision, and close human oversight is recommended, especially in the context of the most critical operations.
High setup costs
An efficient computer vision system includes hardware and software components that are often costly, and the expertise required to develop, configure, and maintain AI models should not be underestimated if they are to generate long-term value. Significant costs can also arise from integrating new AI applications with existing systems, especially in industrial production environments.
Applications of Computer Vision
Computer vision is very often used in synergy with other artificial intelligence techniques, across all areas of business.
Manufacturing industry
Computer vision is used in industrial settings to carry out a wide range of operations, from monitoring the efficiency of production systems to predictive maintenance and quality control of products, to name just a few of its most common applications.
Veicoli autonomi e sistemi di trasporto
Computer vision plays a significant role in autonomous vehicles, performing tasks such as obstacle detection, lane keeping and road sign recognition.
Healthcare and medical diagnosis
Artificial vision is used in medical radiology to carry out diagnostic investigations that are much more accurate than traditional systems, thanks to its ability to see where even the most experienced doctor’s eye could never reach, due to obvious biological limitations. Thanks to artificial vision systems, it is now possible to significantly improve the early diagnosis of many diseases while simultaneously reducing the number of false positives.
Public safety and surveillance
Artificial vision is used in surveillance systems to detect and track people or vehicles, as well as to recognise faces or identification devices, particularly in high-traffic areas such as stadiums, stations, and airports, with the aim of preventing criminal acts or terrorist attacks.
Food industry
Computer vision is widely used in the field of precision agriculture to optimise and make irrigation and fertilisation processes more sustainable. A notable example is the use of drones equipped with artificial vision technologies, which make it possible to monitor crop health (e.g., pest detection) and general soil conditions over very large areas.
Complementary technologies to Machine Vision
Computer vision is an artificial intelligence technique that is employed in an interdisciplinary context, involving other technologies that are highly relevant to the digital transformation of businesses.
IoT sensors
Artificial vision systems often analyse data acquired by IoT systems, whether from video surveillance devices or high-precision cameras used in quality control systems within industrial production.
Convolutional Neural Networks (CNN)
Computer vision uses, on the one hand, machine learning, with algorithms that enable the computer system to learn from visual data, and, on the other hand, convolutional neural networks (CNNs) that decompose images into pixels and assign meaning to them (e.g., through tags).
The term convolution refers to the mathematical operation that allows visual information obtained by the network to be merged by processing neighbouring pixels; this operation aggregates information and thus enables the complete processing of the image. Like deep learning applications, CNNs work iteratively to progressively improve the results achieved.
Cloud Computing and Edge Computing
In numerous application contexts, artificial vision-based applications interface directly with cloud and edge architectures. A very common pipeline in Industry 4.0 involves an IoT sensor capturing an image and sending it to an edge device equipped with computer vision functions for pre-processing, which prepares the data for transmission to the cloud should long-term storage or more in-depth analysis be required.
Moxoff and Computer Vision
The demand for computer vision solutions has never been as high as it is during this particular period, in which companies are innovating their processes and undergoing a profound digital transformation.
Computer vision is capable of innovating traditional processes and enabling new business opportunities. It is, by nature, a complex and interdisciplinary field, requiring concrete knowledge and experience in using a technology stack to find the most effective response to business needs.
Thanks to many years of experience alongside dozens of Italian companies in the field of AI adoption, Moxoff is able to offer consultancy, services and applications that can meet any business objective. This is achieved through a collaborative approach that aims to analyse processes and identify the most suitable computer vision solutions for the specific context.
Once the path to follow has been defined, Moxoff ensures that every aspect of the computer vision project remains closely aligned with the planned business objectives, both in the short and long term, beginning with a detailed implementation roadmap and integration into existing systems.
