Computer vision and everything you need to know about computer vision

One of the most powerful and persuasive types of artificial intelligence is computer vision, which almost all of us have experienced and used in different ways without even knowing it. Here’s a look at what computer vision is, how it works, and why it’s so powerful and awesome. Stay with us in this article from Avir’s artificial intelligence website.

What is computer vision?

Computer vision is a branch of computer science that focuses on replicating parts of the complexity of the human visual system, enabling computers to recognize and process objects in images and videos in the same way that humans recognize objects. Until recently, computer vision only functioned in a limited capacity.

But thanks to advances in artificial intelligence and innovations in deep learning and neural networks, this field has been able to make big leaps in recent years and surpass humans in some tasks related to object recognition and labeling.

One of the factors driving the growth of computer vision is the amount of data we generate today and then use it to train and improve computer vision.

YOLO multi-object detection and classification

Thanks to the massive amount of visual data (more than 3 billion images are shared online on the web every day), the computing power needed to analyze the data is now available. As the computer’s field of view has grown with new hardware and algorithms, the accuracy rates for object recognition have also increased. In less than a decade, today’s systems have gone from 50% to 99% accuracy, being more accurate than humans in responding quickly to visual input.

Early experiments in computer vision began in the 1950s and were first used commercially in the 1970s to distinguish between typed and handwritten text, today the applications of computer vision have grown exponentially.

“By 2022, the computer vision and hardware market is expected to reach $48.6 billion.”

How does computer vision work?

One of the important questions that exist in both neuroscience and machine learning, and which has not yet been given a definitive and correct answer, is how exactly the human brain works and how can we bring it closer to our algorithms? The reality is that there are very few efficient and comprehensive theories of brain computation. So despite the fact that neural networks are supposed to “mimic the way the human brain works,” no one is quite sure if this is actually true.

The same paradox is true for computer vision; Since we don’t have a say in how our brain and eyes process images, it’s hard to say how closely the algorithms used in production approximate our inner mental processes.

Computer vision is largely about pattern recognition. Therefore, one way to train a computer to understand visual data is to feed it thousands or millions of images, labeled if possible; We then subject them to various software techniques or algorithms that allow the computer to hunt down and recognize all the patterns associated with those tags.

So, for example, if you feed a computer millions of pictures of different cats, it will run them all through algorithms that analyze the colors in the picture, the shapes, the distances between the shapes, and the boundaries between them, so that a profile of Defines the meaning of “cat”. When the analysis is done, the computer can (in theory) use its experience and, if given other unlabeled images, find pictures of cats.

Below is a simple illustration of a grayscale image buffer that stores an image of Abraham Lincoln. The brightness of each pixel is represented by an 8-bit number that ranges from 0 (black) to 255 (white):

Pixel data graph. On the left, our image of Lincoln. In the middle, the pixels are labeled with numbers from 0 to 255, indicating their brightness. And on the right, these numbers alone.

{157, 153, 174, 168, 150, 152, 129, 151, 172, 161, 155, 156,

 155, 182, 163, 74, 75, 62, 33, 17, 110, 210, 180, 154,

 180, 180, 50, 14, 34, 6, 10, 33, 48, 106, 159, 181,

 206, 109, 5, 124, 131, 111, 120, 204, 166, 15, 56, 180,

 194, 68, 137, 251, 237, 239, 239, 228, 227, 87, 71, 201,

 172, 105, 207, 233, 233, 214, 220, 239, 228, 98, 74, 206,

 188, 88, 179, 209, 185, 215, 211, 158, 139, 75, 20, 169,

 189, 97, 165, 84, 10, 168, 134, 11, 31, 62, 22, 148,

 199, 168, 191, 193, 158, 227, 178, 143, 182, 106, 36, 190,

 205, 174, 155, 252, 236, 231, 149, 178, 228, 43, 95, 234,

 190, 216, 116, 149, 236, 187, 86, 150, 79, 38, 218, 241,

 190, 224, 147, 108, 227, 210, 127, 102, 36, 101, 255, 224,

 190, 214, 173, 66, 103, 143, 96, 50, 2, 109, 249, 215,

 187, 196, 235, 75, 1, 81, 47, 0, 6, 217, 255, 211,

 183, 202, 237, 145, 0, 0, 12, 108, 200, 138, 243, 236,

 195, 206, 123, 207, 177, 121, 123, 200, 175, 13, 96, 218};

This method of storing image data may go against your expectations, as the data will certainly look two-dimensional when displayed. However, this is the case because computer memory simply consists of an increasing linear list of address spaces.

How to store pixels in memory

Let’s go back to the first picture! Imagine adding a color photo to the first image. Now things get more complicated. Computers usually read color as a set of 3 values—red, green, and blue (RGB)—on the same scale of 0-255. Now, each pixel actually has 3 values ​​for the computer in addition to its position. If we were to color the Lincoln aqueduct, this would result in 12 x 16 x 3 values, or 576 numbers.

This is a lot of memory required for an image and also a lot of pixels for the algorithm to repeat. But to train a model with high accuracy, especially when we’re talking about deep learning, you usually need tens of thousands of images, and the more the better.

Evolution of computer vision

Before the advent of deep learning, the tasks that computer vision could perform were very limited and required manual coding and a lot of effort by human developers and operators. For example, if you wanted to do facial recognition, you would have to do the following:

Creating a database: You had to take separate images of all the subjects you wanted to track, in a specific format.

Annotating images: Then, for each individual image, you had 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 nose, and dozens of other measurements that define each person’s unique features.

Capture new images: Next, you should have captured new images, whether from photos or video content. And then you had to go through the measurement process again and mark the key points on the image. You should also consider the angle from which it is taken.

After all that manual work, the app was finally able to compare the new image’s measurements to those stored in its database and tell you if it matched any of the profiles it was tracking. In fact, there was very little automation and most of the work was done manually. Of course, the margin of error was also very large.

Machine learning offers a different approach to solving computer vision problems. With machine learning, developers no longer need to manually code every single rule into their vision programs. Instead, they programmed “features,” smaller programs that could recognize certain patterns in images. Then they used a statistical learning algorithm such as linear regression, logistic regression, decision tree or support vector machines (SVM) to identify patterns and classify images and identify objects in them.

Machine learning has helped solve many problems that were historically challenging for classical software development tools and approaches. For example, years ago, machine learning engineers were able to create software that could predict breast cancer patients’ chances of survival better than human experts. However, building the software’s features required the efforts of dozens of engineers and breast cancer experts and took a long time to develop.

Deep learning offers a fundamentally different approach to doing machine learning. Deep learning relies on neural networks, a general-purpose function that can solve any problem that can be represented by an example. When you feed a neural network with many labeled samples of a particular type of data, the network can extract common patterns between those samples and turn it into a mathematical equation that helps classify future pieces of information.

For example, creating a face recognition program with deep learning only requires creating or choosing a pre-built algorithm and training it with examples of the faces of the people it needs to recognize. Given enough examples (many examples), the neural network will be able to recognize faces without further instructions on features or measurements.

Deep learning is a very effective way to do computer vision. In most cases, building a good deep learning algorithm involves collecting a large amount of labeled training data and setting parameters such as the type and number of neural network layers and training courses. Compared to previous types of machine learning, deep learning is easier and faster to develop and deploy.

Most current computer vision applications such as cancer diagnosis, self-driving cars, and face recognition use deep learning. Deep learning and deep neural networks have moved from the conceptual realm to practical applications thanks to the availability and advancements in cloud computing hardware and resources.

How long does it take to decode an image?

In short, if we want to answer, we must say that it will not take long. This is the key to why computer vision is so exciting: In the past, even supercomputers could take days or weeks or even months to do all the calculations needed, but today’s superfast chips and related hardware, along with fast Internet and Reliable and cloud networks speed up the process. In the past, the willingness of many large companies doing AI research to share their work with Facebook, Google, IBM, and Microsoft, especially open-sourcing some of their machine learning work, has been an important factor.

This sharing allows others to advance their work faster with the help of other people’s work instead of starting from scratch. As a result, the AI ​​industry is gearing up, and tests that took weeks not long ago can now take 15 minutes. And for many real-world applications of computer vision, this process happens consistently within a few hundredths of a second, so that a modern computer can be what scientists call “situationally aware.”

Applications of computer vision

Computer vision is one area of ​​machine learning where core concepts are being integrated into major products that we use every day. Next, we will discuss the applications of computer vision.

1. Vision computer and its application in self-driving cars

It’s not just tech companies that are using machine learning for visual applications. Computer vision enables self-driving cars to understand their surroundings. Cameras take video from different angles around the car and feed it to computer vision software. The software then processes the images in real time to find road ends, read traffic signs, and identify other cars, objects, and pedestrians. Finally, the self-driving car will be able to navigate the streets and highways, avoid obstacles and safely deliver its passengers to their destination.

2. Computer vision and its application in face recognition

Computer vision also plays an important role in facial recognition applications, a technology that enables computers to match images of people’s faces to their identities. Computer vision algorithms identify facial features in images and then compare them to databases of facial profiles. User devices use facial recognition to authenticate their owners. Social media applications use facial recognition to identify and tag users. Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds.

3. Computer vision and its application in augmented reality and mixed reality

Computer vision also plays an important role in augmented reality and mixed reality, technology that enables computing devices such as smartphones, tablets, and smart glasses to superimpose virtual objects on real-world images. Using computer vision, augmented reality equipment recognizes objects in the real world to determine locations on the device’s display to place a virtual object. For example, computer vision algorithms can help augmented reality applications identify surfaces such as tables, walls, and floors, which is a critical part of determining depth and dimensions and placing virtual objects in the physical world.

4. Computer vision and its application in health care

Computer vision has also been an important factor in advances in health technology. Computer vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding signs in X-ray and MRI scans.

Important challenges of computer vision

Helping computers to see like humans is a very difficult task. Inventing a machine that looks like us is a very difficult task, not just because it’s hard to get computers to do it, but because we’re not entirely sure how human vision works in the first place.

The study of biological vision requires an understanding of the organs of perception, such as the eyes, as well as the interpretation of perception within the brain. Much progress has been made both in mapping the process and in discovering the tricks and shortcuts used by the system, although as with any study involving the brain, there is a long way to go.

Computer vision tasks

Many common computer vision applications involve trying to recognize objects in photographs. For example:

Object Classification: What is the broad classification of objects in this photo?

Object Identification: What type of a certain object is there in this photo?

Object Verification: Is the object in the photo?

Object Detection: Where are the objects in the photo?

Object Landmark Detection: What are the key points for the object in the photo?

Object Segmentation: Which pixels belong to the object in the image?

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

Apart from the mere detection of objects, other methods of analysis are:

It uses computer vision to estimate the speed of objects in a video or the camera itself. This system is among the products of Avir company and you can use the Avir video motion analysis system with high accuracy.

Contact Avir now! The button should be designed for the contact number

In image segmentation, algorithms divide images into several sets of views.

Scene reconstruction creates a 3D model of the scene with input from images or video.

In image restoration, noise such as blurring is removed from photos using machine learning-based filters.

Any other program that involves understanding pixels through software can be safely labeled as computer vision.

And at the end of that…

Although recent advances in artificial intelligence have been impressive, computer vision has not yet fully developed. However, there are now numerous healthcare institutions and companies that have found ways to apply computer vision systems, backed by convolutional neural networks (CNNs), to real-world problems. This trend will not stop anytime soon.