Computer vision is one of the most powerful and compelling AI technologies, and you must have already experienced it in various ways without even realizing it. But what exactly is computer vision? How does it work? Why is it so good?

What is computer vision?

Computer vision is the field of computer science that focuses on replicating part of the complexity of the human visual system and enabling computers to recognize and process objects in images and video in the same way that humans do. So far, the power of computer vision has not been fully exploited.

Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has made qualitative leaps in recent years, outpacing humans in certain tasks related to detecting and labeling objects. One of the driving factors behind the growth of computer vision is the amount of data we generate today, which is then used to train and improve computer vision.

With vast amounts of visual data (3 billion images shared online every day), the computing power needed to analyze the data can now be accessed. As the field of computer vision evolves, as new hardware and algorithms develop, so does the accuracy of object recognition. In less than a decade, today’s AI systems have gone from being 50 percent accurate to 99 percent accurate, making them more accurate than humans when responding quickly to visual input.

Early experiments in computer vision began in the 1950s and were first used commercially to distinguish typed text from handwritten text in the 1970s, with the use of computer vision growing exponentially today. The computer vision and hardware market is expected to reach $48.6 billion by 2022.

How does computer vision work?

One of the main open questions in neuroscience and machine learning is: How exactly do our brains work, and how do we approximate it with our own algorithms? The reality is that there are very few working and comprehensive theories of brain computation. So despite the fact that neural networks are supposed to “mimic the way the brain works,” no one is sure if this is really true.

The same paradox applies to computer vision. Since we haven’t yet decided how our brains and eyes process images, it’s hard to say how closely the algorithms used in production approximate our internal mental processes.

In a way, computer vision is about pattern recognition. So one way to train a computer to understand visual data is to feed it thousands of images, tagged images, and then subject them to various software techniques or algorithms that allow the computer to track them.

For example, if you feed a computer a million images of cats, it will subject them all to algorithms that allow them to analyze the colors in the photo, the shapes, the distances between the shapes, the positions of objects next to each other, and so on, so that it can recognize the meaning of “cat.” Once done, the computer will theoretically be able to use its experience to identify the cat if it provides other unlabeled images to look for images belonging to the cat.

Evolution of computer vision

Before deep learning, computer vision could perform a very limited number of tasks and required a lot of manual coding and work by developers and human operators.

For example, if face recognition is to be performed, the following steps must be performed:

** Create database: ** You must capture a single image of all the topics you want to track in a specific format.

** Add notes to images: ** For each individual image, you have to enter several key data points, such as the distance between the eyes, the width of the bridge of the nose, the distance between the upper lip and the nose and dozens of other measurements that define unique characteristics for each person.

** Capture new image: ** Next, you must capture new image, whether photo or video content. Then you have to perform the measurement process again, marking key points on the image. You also have to consider the Angle of the image.

After all this manual work, the application will eventually be able to compare the measurements in the new image to those stored in the database and tell you if it corresponds to any of the profiles being tracked. In practice, very little automation is involved and most of the work is done manually. And the margin of error is still large.

Machine learning offers an alternative approach to computer vision problems. With machine learning, developers no longer need to manually code every rule into their visual applications. Instead, they write “features,” smaller applications that detect specific patterns in images. They then use statistical learning algorithms (such as linear regression, logistic regression, decision trees or support vector machines (SVM)) to detect patterns, classify images and detect objects within them.

Machine learning has helped solve many problems that have historically been challenging for traditional software development tools and methods. A few years ago, for example, machine learning engineers were able to create software that could predict breast cancer better than human experts. But building the software’s functionality required the work of dozens of engineers and breast cancer specialists, and took a lot of time to develop.

About Deep Learning

Deep learning offers a completely different approach to machine learning. Deep learning relies on neural networks, a general-purpose capability that can solve any problem represented by an example. When you supply a neural network with many examples labeled with specific types of data, it will be able to extract common patterns between these examples and turn them into mathematical equations that help classify future information.

For example, creating facial recognition applications using deep learning simply requires developing or choosing a pre-built algorithm and training it with examples of faces that must be detected. Given enough examples (lots of them), the neural network will be able to detect faces without further specification of features or measurements.

Deep learning is a very effective method for computer vision. In most cases, creating good deep learning algorithms boils down to collecting large amounts of labeled training data and adjusting parameters, such as neural networks and the type and number of layers of training periods. Deep learning is simple and fast to develop and deploy compared to previous types of machine learning.

Most current computer vision applications (such as cancer detection, self-driving cars and face recognition) utilize deep learning. Due to availability and advances in hardware and cloud computing resources, deep learning and deep neural networks have moved from conceptual domains to practical applications.

How long it will take to decrypt the image

Not much. The key to this is why so exciting computer vision: in the past, even the computer may also need a few days, weeks or even months to complete all the required calculation, and the ultra-high speed and related hardware chips and fast, reliable network of the Internet, and cloud lightning fast to process. What was once crucial was the willingness of many large companies engaged in AI research, such as FB, Google, IBM and Microsoft, to share their work, especially by open-source some of their machine learning work.

That way, others can continue to work instead of starting from scratch. As a result, the AI industry continues to evolve, and experiments that not long ago took weeks to complete may take 15 minutes today. For many practical applications of computer vision, this process takes place in a succession of microseconds, so today’s computers can become what scientists call “situational awareness.”

Computer vision applications

Computer vision is one of the fields of machine learning where core concepts have been integrated into the products we use every day.

Self-driving car

Technology companies are not the only ones using machine learning to develop imaging applications. Computer vision allows driverless cars to sense their surroundings. The cameras capture video from different angles around the car and feed it to computer vision software, which then processes the images in real time to find the end of the road, read traffic signs and detect other cars, objects and pedestrians. Self-driving cars can then drive on streets and highways, avoiding obstacles and (hopefully) safely getting their passengers to their destinations.

Face recognition

Computer vision also plays an important role in facial recognition applications, which enable computers to match images of faces to their identities. Computer vision algorithms detect facial features in the images and compare them with facial contour databases. Consumer devices use facial recognition to verify the identity of their owner. Social media apps use facial recognition to detect and tag users. Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds.

AR/VR augmented reality and mixed reality

Computer vision also plays an important role in AR/VR (augmented and mixed reality), which enables computing devices such as smartphones, tablets and smart glasses to overlay and embed virtual objects into real-world images. Using computer vision, AR devices can detect objects in the real world to determine where to place virtual objects on the device’s display. For example, computer vision algorithms can help AR applications detect flat surfaces such as desktops, walls, and floors, an important part of determining depth and dimensions and placing virtual objects in the physical world.

Healthcare industry

Computer vision is also an important part of advances in health technology. Computer vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in X-ray and MRI scans.

Security video surveillance

Following images, video structuring has become a hot topic in deep learning. Video content is undoubtedly more complex than images. Video structured description technology can transform surveillance video into information that can be understood by human and machine. At present, video structuring technology has been applied to security video surveillance.

For example, The EasyCVR intelligent video analysis and security monitoring platform of TSINGSEE Qingxi Video, based on AI and big data analysis, can realize face recognition, target detection, license plate recognition and vehicle type analysis of information in videos, which is widely used in smart transportation, smart city and smart security scenes.

The challenges of computer vision

Helping computers see information is very difficult. Inventing a machine that looks like us is a deceptively difficult task, not only because it is hard to make a computer do it, but also because we are not entirely sure how human vision works.

The study of biological vision requires an understanding of sensory organs such as the eye and the interpretation of perception within the brain. Great progress has been made both in charting flow charts and in discovering tricks and shortcuts to use in the system, although, as with any research involving the brain, there is still a long way to go.

A common term for computer vision

Many popular computer vision applications involve trying to recognize things in photos. Such as:

Object classification: What are the general categories of objects in this photo?

Object recognition: What is the type of object given in the photo?

Object verification: Is there an object in the photo?

Object Detection: Where is the object in the photo?

Object landmark detection: What are the key points of the object in the photo?

Object segmentation: Which pixels do the objects in the image belong to?

Object recognition: What objects are in this photo and where are they?

In addition to objective understanding, other methods of analysis include:

Video motion analysis uses computer vision to estimate the speed of objects in the video or the camera itself.

In image segmentation, the algorithm divides the image into multiple groups of views.

Scene reconstruction creates a 3D model of a scene input through images or video.

In image restoration, machine learning-based filters are used to remove noise, such as blur, from a photograph.

conclusion

Despite recent impressive advances, we haven’t even solved the computer vision problem yet. But several organizations and AI companies have found ways to apply neural network-driven C computer vision systems to practical problems. Moreover, machine learning and deep learning under AI have become the trend.

From the perspective of application, the huge market potential of video surveillance technology based on computer vision provides a broad application prospect for video structuring technology.