For most people, identifying objects surrounding them is an easy task.

Let’s say you’re in your office. You can probably casually list objects like desks, computers, filing cabinets, printers, and so on. While this action seems simple on the surface, human vision is actually quite complex.

So, it’s not surprising that computer vision – a relatively new branch of technology aiming to replicate human vision – is equally, if not more, complex.

But before we dive into these complexities, let’s understand the basics – what is computer vision?

Computer vision is an artificial intelligence (AI) field focused on enabling computers to identify and process objects in the visual world. This technology also equips computers to take action and make recommendations based on the visual input they receive.

Simply put, computer vision enables machines to see and understand.

Learning the computer vision definition is just the beginning of understanding this fascinating field. So, let’s explore the ins and outs of computer vision, from fundamental principles to future trends.

History of Computer Vision

While major breakthroughs in computer vision have occurred relatively recently, scientists have been training machines to “see” for over 60 years.

To do the math – the research on computer vision started in the late 1950s.

Interestingly, one of the earliest test subjects wasn’t a computer. Instead, it was a cat! Scientists used a little feline helper to examine how their nerve cells respond to various images. Thanks to this experiment, they concluded that detecting simple shapes is the first stage in image processing.

As AI emerged as an academic field of study in the 1960s, a decade-long quest to help machines mimic human vision officially began.

Since then, there have been several significant milestones in computer vision, AI, and deep learning. Here’s a quick rundown for you:

  • 1970s – Computer vision was used commercially for the first time to help interpret written text for the visually impaired.
  • 1980s – Scientists developed convolutional neural networks (CNNs), a key component in computer vision and image processing.
  • 1990s – Facial recognition tools became highly popular, thanks to a shiny new thing called the internet. For the first time, large sets of images became available online.
  • 2000s – Tagging and annotating visual data sets were standardized.
  • 2010s – Alex Krizhevsky developed a CNN model called AlexNet, drastically reducing the error rate in image recognition (and winning an international image recognition contest in the process).

Today, computer vision algorithms and techniques are rapidly developing and improving. They owe this to an unprecedented amount of visual data and more powerful hardware.

Thanks to these advancements, 99% accuracy has been achieved for computer vision, meaning it’s currently more accurate than human vision at quickly identifying visual inputs.

Fundamentals of Computer Vision

New functionalities are constantly added to the computer vision systems being developed. Still, this doesn’t take away from the same fundamental functions these systems share.

Image Acquisition and Processing

Without visual input, there would be no computer vision. So, let’s start at the beginning.

The image acquisition function first asks the following question: “What imaging device is used to produce the digital image?”

Depending on the device, the resulting data can be a 2D, 3D image, or an image sequence. These images are then processed, allowing the machine to verify whether the visual input contains satisfying data.

Feature Extraction and Representation

The next question then becomes, “What specific features can be extracted from the image?”

By features, we mean measurable pieces of data unique to specific objects in the image.

Feature extraction focuses on extracting lines and edges and localizing interest points like corners and blobs. To successfully extract these features, the machine breaks the initial data set into more manageable chunks.

Object Recognition and Classification

Next, the computer vision system aims to answer: “What objects or object categories are present in the image, and where are they?”

This interpretive technique recognizes and classifies objects based on large amounts of pre-learned objects and object categories.

Image Segmentation and Scene Understanding

Besides observing what is in the image, today’s computer vision systems can act based on those observations.

In image segmentation, computer vision algorithms divide the image into multiple regions and examine the relevant regions separately. This allows them to gain a full understanding of the scene, including the spatial and functional relationships between the present objects.

Motion Analysis and Tracking

Motion analysis studies movements in a sequence of digital images. This technique correlates to motion tracking, which follows the movement of objects of interest. Both techniques are commonly used in manufacturing for monitoring machinery.

Key Techniques and Algorithms in Computer Vision

Computer vision is a fairly complex task. For starters, it needs a huge amount of data. Once the data is all there, the system runs multiple analyses to achieve image recognition.

This might sound simple, but this process isn’t exactly straightforward.

Think of computer vision as a detective solving a crime. What does the detective need to do to identify the criminal? Piece together various clues.

Similarly (albeit with less danger), a computer vision model relies on colors, shapes, and patterns to piece together an object and identify its features.

Let’s discuss the techniques and algorithms this model uses to achieve its end result.

Convolutional Neural Networks (CNNs)

In computer vision, CNNs extract patterns and employ mathematical operations to estimate what image they’re seeing. And that’s all there really is to it. They continue performing the same mathematical operation until they verify the accuracy of their estimate.

Deep Learning and Transfer Learning

The advent of deep learning removed many constraints that prevented computer vision from being widely used. On top of that, (and luckily for computer scientists!), it also eliminated all the tedious manual work.

Essentially, deep learning enables a computer to learn about visual data independently. Computer scientists only need to develop a good algorithm, and the machine will take care of the rest.

Alternatively, computer vision can use a pre-trained model as a starting point. This concept is known as transfer learning.

Edge Detection and Feature Extraction Techniques

Edge detection is one of the most prominent feature extraction techniques.

As the name suggests, it can identify the boundaries of an object and extract its features. As always, the ultimate goal is identifying the object in the picture. To achieve this, edge detection uses an algorithm that identifies differences in pixel brightness (after transforming the data into a grayscale image).

Optical Flow and Motion Estimation

Optical flow is a computer vision technique that determines how each point of an image or video sequence is moving compared to the image plane. This technique can estimate how fast objects are moving.

Motion estimation, on the other hand, predicts the location of objects in subsequent frames of a video sequence.

These techniques are used in object tracking and autonomous navigation.

Image Registration and Stitching

Image registration and stitching are computer vision techniques used to combine multiple images. Image registration is responsible for aligning these images, while image stitching overlaps them to produce a single image. Medical professionals use these techniques to track the progress of a disease.

Applications of Computer Vision

Thanks to many technological advances in the field, computer vision has managed to surpass human vision in several regards. As a result, it’s used in various applications across multiple industries.

Robotics and Automation

Improving robotics was one of the original reasons for developing computer vision. So, it isn’t surprising this technique is used extensively in robotics and automation.

Computer vision can be used to:

  • Control and automate industrial processes
  • Perform automatic inspections in manufacturing applications
  • Identify product and machine defects in real time
  • Operate autonomous vehicles
  • Operate drones (and capture aerial imaging)

Security and Surveillance

Computer vision has numerous applications in video surveillance, including:

  • Facial recognition for identification purposes
  • Anomaly detection for spotting unusual patterns
  • People counting for retail analytics
  • Crowd monitoring for public safety

Healthcare and Medical Imaging

Healthcare is one of the most prominent fields of computer vision applications. Here, this technology is employed to:

  • Establish more accurate disease diagnoses
  • Analyze MRI, CAT, and X-ray scans
  • Enhance medical images interpreted by humans
  • Assist surgeons during surgery

Entertainment and Gaming

Computer vision techniques are highly useful in the entertainment industry, supporting the creation of visual effects and motion capture for animation.

Good news for gamers, too – computer vision aids augmented and virtual reality in creating the ultimate gaming experience.

Retail and E-Commerce

Self-check-out points can significantly enhance the shopping experience. And guess what can help establish them? That’s right – computer vision. But that’s not all. This technology also helps retailers with inventory management, allowing quicker detection of out-of-stock products.

In e-commerce, computer vision facilitates visual search and product recommendation, streamlining the (often frustrating) online purchasing process.

Challenges and Limitations of Computer Vision

There’s no doubt computer vision has experienced some major breakthroughs in recent years. Still, no technology is without flaws.

Here are some of the challenges that computer scientists hope to overcome in the near future:

  • The data for training computer vision models often lack in quantity or quality.
  • There’s a need for more specialists who can train and monitor computer vision models.
  • Computers still struggle to process incomplete, distorted, and previously unseen visual data.
  • Building computer vision systems is still complex, time-consuming, and costly.
  • Many people have privacy and ethical concerns surrounding computer vision, especially for surveillance.

Future Trends and Developments in Computer Vision

As the field of computer vision continues to develop, there should be no shortage of changes and improvements.

These include integration with other AI technologies (such as neuro-symbolic and explainable AI), which will continue to evolve as developing hardware adds new capabilities and capacities that enhance computer vision. Each advancement brings with it the opportunity for other industries (and more complex applications). Construction gives us a good example, as computer vision takes us away from the days of relying on hard hats and signage, moving us toward a future in which computers can actively detect, and alert site foremen too, unsafe behavior.

The Future Looks Bright for Computer Vision

Computer vision is one of the most remarkable concepts in the world of deep learning and artificial intelligence. This field will undoubtedly continue to grow at an impressive speed, both in terms of research and applications.

Are you interested in further research and professional development in this field? If yes, consider seeking out high-quality education in computer vision.

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Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 6 min read

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By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

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Medium: First cohort of students set to graduate from Open Institute of Technology
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 4 min read

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  • Medium, published on March 24th, 2025

By Alexandre Lopez

The first ever cohort will graduate from Open Institute of Technology (OPIT) on 8th March 2025, with 40 students receiving a Master of Science degree in Applied Data Science and AI.

OPIT was launched two years ago by renowned edtech entrepreneur Riccardo Ocleppo and Prof. Francesco Profumo (former minister of education in Italy), who witnessed the growing tech skills gap and wanted to combat it directly through creating a brand-new, accredited academic institution focused on innovative BSc and MSc degrees in the field of Technology.

The higher education institution has grown since its initial launch. Having started with just two degrees on offer — BSc in Modern Computer Science and an MSc in Applied Data Science and Artificial Intelligence — OPIT now offers two bachelor’s and four master’s degrees in a range of areas, such as Computer Science, Digital Business, Artificial Intelligence and Enterprise Cybersecurity.

Students at OPIT can learn from a wide range of professors who combine academic and professional expertise in software engineering, cloud computing, AI, cybersecurity, and much more. The institution operates on a fully remote system, with over 300 students tuning in from 78 countries around the world.

80% of OPIT’s students are already working professionals who are currently employed at top companies across many industries. They are in global tech firms like Accenture, Cisco, and Broadcom and financial companies such as UBS, PwC, Deloitte, and First Bank of Nigeria. Some are leading innovation at Dynatrace and Leonardo, while others focus on sustainability and social impact with Too Good To Go, Caritas, and the Pharo Foundation. From AI and software development to healthcare and international organizations like NATO and the United Nations Mine Action Service (UNMAS), OPIT alumni are making a real difference in the world.

OPIT is working on the development of the expansion of our current academic offerings, new courses, doctoral programs, applied research, and technology transfer initiatives with companies.

Once in the program, students have flexible options to complete their studies faster (by studying during the summer) or extend their studies longer than the standard duration. Every OPIT degree ends with a “capstone project”, providing them with real-life experiences in relevant businesses and industries. Some examples of capstone projects include “AI in Anti-Money Laundering: Leveraging AI to combat financial crime,” or “Predictive Modeling for Climate Disasters: Using AI to anticipate climate-related emergencies.”

The graduation on March 8th marks a pivotal moment for OPIT.

“The success of this first class of graduates marks a significant milestone for OPIT and reinforces our mission: to provide high-quality, globally accessible tech education that meets the ever-evolving demands of the job market,” said Riccardo Ocleppo, founder of OPIT.

“In just two years, we have built a dynamic and highly professional learning environment, attracting students from all over the world and connecting them with leading companies.”

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