Computer vision is a technology that allows computers to interpret and analyse images and videos in a similar way as the human brain does. Artificial intelligence (AI) algorithms can identify objects, recognise faces and analyse movements.
Computer vision finds application in the most diverse areas of everyday life, from Industry 4.0 to automotive, from medicine to surveillance. In this article, we will explore the different areas of application, the advantages it offers and possible future developments.
Table of contents
What is computer vision?
Computer vision is a branch of artificial intelligence; it deals with teaching computers to interpret digital images, both static and moving (video).
The term vision refers to the ability to recognise and understand objects, scenes, actions, emotions and relationships in a digital image.
Computer vision AI algorithms are based on deep learning and machine learning techniques, which allow useful information to be extracted and used for various purposes.
How does it work?
Computer vision uses countless mathematical and statistical techniques to process images and videos. Some of the most common techniques include:
- segmentation of the image, i.e. the division into smaller parts;
- feature extraction, i.e. the identification of distinctive elements;
- pattern recognition, i.e. the search for repetitive sequences.
We will now list the main steps of computer vision in more detail.
1. Image acquisition
This involves capturing the image from a source, such as a camera, scanner, sensor or digital archive. The image can be in bitmap, vector or mixed format, depending on the type of information it contains.
2. Pre-processing
Image quality is improved by reducing noise, contrast, blurring and other factors that can affect sharpness and resolution. Techniques such as image filtering, transformation, segmentation and normalisation can be used.
3. Extraction of characteristics
This involves identifying the salient elements of the image, such as edges, contours, colours, textures, key points and regions of interest. In this case, techniques such as corner detection, descriptor description, feature coding and clustering can be used.
4. Recognition and classification
The features extracted from the image are associated with predefined categories, such as object classes, people faces, emotional expressions, actions, scenes, texts and symbols. Machine learning and deep learning techniques such as artificial neural networks, support vector machines, probabilistic models and random forests intervene here.
5. Interpretation and understanding
Information obtained from recognition and classification is given meaning, based on context, purpose and logic. Reasoning, inference, learning and decision-making techniques can be used to generate answers, predictions, recommendations or actions.
Industry 4.0
In the era of Industry 4.0, thanks to computer vision, factories will be able to reduce costs, monitor production chains in real time and detect any anomalies or defects in finished products. This will make it possible to prevent defects before they become a problem and reduce the number of discarded products.
Computer vision will also improve safety in the workplace. AI-based surveillance systems will detect dangers and warn workers in good time.
Automotive: safety and autonomous driving
Computer vision applied to the automotive industry will make it possible to detect and identify objects on the road, such as pedestrians, vehicles and signs. Driver assistance systems will thus alert the driver to hazards, such as the presence of a passer-by at a zebra crossing or approaching a curve.
Medicine: personalised diagnosis and treatment
Computer vision will also revolutionise medicine. It will make it possible to improve the diagnosis of diseases and to develop personalised therapies based on the individual characteristics of patients.
By analysing images obtained through an MRI or computed tomography scan, algorithms will be able to accurately identify tumours, aneurysms and other pathologies.
In the case of tumours, computer vision will make it possible to identify the most effective treatment to fight them. It is important to emphasise, however, that this technology will never replace the role of the doctor: the final decision on diagnosis and treatment will always be taken by the healthcare professional.
Security: surveillance and facial recognition
Computer vision-based surveillance systems will detect potentially dangerous situations such as intrusion into a restricted area or theft of valuables. These systems will be used to monitor large areas such as airports, stadiums and public squares.
Facial recognition will make it possible to identify unknown or dangerous persons and possibly report them to the competent authorities. Computer vision will also prove to be a valuable ally against terrorist attacks or criminal acts.
Technological developments and new frontiers
The future of computer vision is extremely promising. All AI-based technologies will become increasingly sophisticated, enabling levels of precision that were unthinkable a few years ago.
One of the main future developments in computer vision will certainly concern machine learning. Thanks to machine learning, AI will learn from its mistakes and continuously improve its performance.
Computer vision will also have a significant impact on agriculture. Cameras and sensors analysing plants in real time will enable farmers to monitor the state of their crops and detect any diseases or infestations.
Finally, computer vision will also have a significant impact on robotics, manufacturing and
space exploration.
However, the use of computer vision also raises certain ethical and legal issues that need to be considered as soon as possible. It is important that the development of new technologies takes place in a responsible manner that respects human rights.
Want to develop a computer vision project? Rely on PMF Research
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PMF Research is a research and development (R&D) centre established in 2003 and belonging to the JO Group cluster of companies; it deals with ICT, virtual reality, artificial intelligence and big data.
