What is Computer Vision? Know Computer Vision Basic to Advanced & How Does it Work?
Computer vision is a research area that allows computers to replicate the human visual system. It’s a subset of artificial intelligence that collects information from digital images or videos and processes it to define attributes. This process involves data collection, screening, analysis, identification, and extraction. This extensive processing helps the computer understand the visual content and respond accordingly. You can also take a free computer vision course to understand the basics of the field of artificial intelligence.
To collect multidimensional data, computer vision projects transform digital visual content into explicit descriptions. This data is then translated into a computer-readable language to aid in decision-making. The main goal of this branch of artificial intelligence is to teach machines to collect information from pixels.
A brief history of computer vision
The history of computer vision started like all the great things in the world of technology, it started with cats. Two Swedish scientists, Hubel and Wiesel, put the cat in a restraint harness and the electrodes in the visual cortex. Scientists showed the cat a set of images via a projector, hoping the brain cells in the visual cortex would begin to fire. The image failed, but the Eureka moment occurred when the projector slide was removed, and a horizontal line of light appeared on the wall. Neurons ignited, causing a crackling electrical noise. Scientists have just discovered that the early layers of the visual cortex respond to simple shapes such as lines and curves, much like the earlier layers of deep neural networks.
This experiment is the beginning of understanding the relationship between computer vision and the human brain and will help us understand artificial neural networks.
However, before the advent of the cat’s brain, it was a university that pioneered artificial intelligence, and analog computer vision began in the 1950s.
Computer Vision and Human Vision
The idea that computer vision must be derived from animal vision was predominant in 1959 when the above neurophysiologists tried to understand cat vision.
Since then, computer vision history has been littered with milestones brought about by the rapid development of image acquisition and scanning equipment complemented by the design of state-of-the-art image processing algorithms.
In the 1960s, AI emerged as a discipline, and in 1974 the first robust optical character recognition system was developed. These are also called the types of computer vision.
In 2010, the ImageNet dataset was born with millions of labeled images available for free for research purposes. Two years later, the AlexNet architecture was formed and became one of the most important breakthroughs in computer vision, cited more than 82,000 times.
How does computer vision work?
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Images on your computer are often stored as a large grid of pixels. Each pixel is defined as a color stored as a combination of the three additive primary colors of RGB (red, green, blue). These are combined in different intensities to represent different colors. Colors are stored in pixels.
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Consider a simple algorithm for tracking a bright orange soccer ball on the field. To do this, get the RGB value of the center pixel. You can use this stored value to provide an image to your computer program and ask you to find the pixel with the best color match. The algorithm examines each pixel simultaneously and calculates the difference from the target color. Looking at all the pixels, the best game is probably the pixels from the orange ball. You can run this algorithm on every video frame to track the ball over time. However, the algorithm can be confusing if one of the teams wears an orange jersey. Therefore, this approach is more important than a single pixel, e.g., the Edges of an object composed of many pixels.
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Image processing algorithms are so-called patches to identify these features in an image. You need to consider a small pixel area called, for example, an algorithm that finds vertical edges in the scene so that the drone can safely navigate the field of obstacles. This operation uses a mathematical notation called a kernel or filter. It contains the value of the multiplication per pixel, the sum of which is stored in the center pixel.
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Before an algorithm called Viola-Jones, face detection combined multiple kernels to detect facial features. Today, the latest trend algorithms are on block convolutional neural networks (CNNs).
CNN does not have to be many layers deep but is usually intended to detect complex objects and scenes. This technique is called deep learning.
Recent advances have also made computers able to track and identify hand gestures. But what’s exciting is that scientists are still in their infancy and are facilitated by advances in computing technologies such as ultra-fast GPUs. The
CNN applies to many non-face image recognition tasks such as handwritten text recognition, tumor detection on CT scans, and road traffic flow monitoring.
Where we can apply computer vision technology
Many people think that computer vision is something from the distant future of design. It’s not true. Computer vision is already fused into many areas of our lives. Some of them are shown below,
Content organization
Computer vision systems help to organize our content. Apple Photos is a great example. The app has to access our photo collections, automatically tags photos, and allows us to browse through a more structured photo collection.
Face recognition
Facial recognition technology matches photographs of people’s faces with their identities. This technology is integrated into essential products that we use every day. For example, Facebook uses computer vision to recognize people in photos.
Facial recognition is an essential technology for biometric authentication. Many mobile devices allow users to unlock the device by displaying their faces. The front camera uses facial recognition; mobile devices process this image and, based on analysis, can determine whether the person holding the device is authorized.
Self-driving cars
Computer vision enables cars to understand their surroundings. An intelligent vehicle has multiple cameras that capture different angles and send videos as input to computer vision software. The system processes video in real-time and detects objects such as traffic signs, objects near the car (like pedestrians or other vehicles), traffic lights, etc. The best example of this technology is the autopilot in Tesla cars.
Conclusion
Computer vision is a popular topic in new technology articles. A different procedure for using data is what pushes this technology forward. The vast amount of information we generate daily, which some think is the bane of our generation, is actually being used to our advantage data can teach computers to see and understand objects. This technology also demonstrates a significant step our civilization is taking towards creating artificial intelligence that will be as sophisticated as humans.
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