Computer Vision: A Comprehensive Guide to Techniques and Applications
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.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.