There would be no wrong in saying that computer vision is one of the most powerful and compelling types of artificial intelligence. You have definitely faced this technology without even knowing. Thanks to the technological innovations, advancements, and compelling neural networks artificial intelligence and deep learning, that field is skyrocketing at a neck-breaking pace. Such technologies have been able to surpass humans in some tasks related to object detection and labeling.
It is estimated that,
The computer vision and hardware market is expected to reach $48.6 billion by the year 2022.
Keep reading to know more about computer vision and how it is leaving an impact on our everyday lives.
What is Computer Vision?
In layman’s terms, Computer vision is basically a field that comes under the umbrella of artificial intelligence. This compelling technology instructs computers to interpret and understand the visual world. Utilization of digital images from videos, cameras, deep learning models, and machines can accurately classify and identify objects, and then respond to what they “see”.
How Does Computer Vision Work?
“How exactly does a human brain work?” is one of the major questions that has been on the tongue’ s tip of those who belong to neuroscience, machine learning, and the IT industry. In fact, there are very few comprehensive and working theories of brain computation. So, despite the fact that Neural Nets are supposed to “imitate the way the human brain works”, still nobody is sure if that is actually true.
However, the following are the three basic steps on the basis of which computer vision works:
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Step no. 1: Acquiring an image
Computer vision technology acquires pictures, even large sets in real-time via video, photos, or 3D technology for analysis.
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Step no. 2: Processing the image
Deep learning models play a pivotal role in automating much of this process, but the models are usually trained by first being fed a plethora of pre-identified or labeled images.
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Step no. 3: Understanding the image
The interpretative step is the final step. In this step, computer vision technology identifies and classifies the object.
Types of Computer Vision
Artificial intelligence (AI) systems of this modern age can go one step ahead and take actions on the basis of understanding the image. There are several types of computer vision and different use cases. Let’ s have a look at them.
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Pattern Detection: Pattern detection is a process of recognizing colors, shapes, and several other indicators in images.
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Image classification: Image classification refers to the grouping of images into different categories.
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Feature Matching: Feature matching is a type of pattern detection that matches similarities in images for their classification.
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Edge Detection: Edge detection is a technique that is used to identify the outside edge of a landscape or object for the better identification of what is in the image.
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Facial Recognition: Facial recognition is an advanced type of object detection that identifies a human face in an image, and also identifies a specific individual.
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Image Segmentation: Image segmentation partitions an image into several regions or pieces to be examined separately.
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Object Detection: Object detection refers to the identification of a particular object in an image. Advanced object detection recognizes several objects in a single picture. For example, a football field, a defensive player, an offensive player, and so on. Such models use X coordinate and Y coordinate for the creation of a bounding box and for the identification of everything inside the box.
Simple applications of computer vision probably only utilize one of the techniques discussed above. More advanced uses, such as computer vision for self-driving cars rely on multiple techniques for the accomplishment of their goals.
Applications of Computer Vision
Computer vision is one of the most pivotal areas in machine learning where major concepts are already being integrated into major products that we use in our everyday lives. Let’ s have a look at the applications of computer vision.
Computer Vision in Self-driving Cars
Computer vision permits self-driving cars to make sense of their circumstances. The camera records video from different angles around the car and feeds it to computer vision software, which, afterward processes the pictures in real-time. That processing of image helps to find the extremities of roads, detect other cars, and read traffic signs, objects, and pedestrians.
Computer Vision in Facial Recognition
There would be no wrong in saying that computer vision plays a significant role in facial recognition applications. This compelling technology allows the computer to match images of people’ s faces to their identities. Top-notch computer vision algorithms detect facial features in pictures and then compare them with databases of fake profiles. Law enforcement authorities majorly depend on facial recognition technology to recognize criminals in video feeds.
Computer Vision in Mixed Reality and Augmented Reality
Computer vision plays an important role in augmented reality and mixed reality. This technology allows computing devices such as tablets, smartphones, and smart glasses to overlay and implant virtual objects on real-world imagery. Augmented reality (AR) detect objects in the real world with the help of computer vision, to determine the location on a device’ s display to place a virtual object.
Computer Vision in Healthcare
Computer vision technology plays a significant role and is an important part of advances in health tech. It’s algorithms can assist automate tasks. For example detecting cancerous moles in skin images, detecting symptoms in x-ray and MRI scans.
Conclusion
Despite the recent progress in technological innovations and advancements, which is impressive, we are still not even close to decrypting computer vision. At the heart of it all, there are already several enterprises and healthcare institutions that have found ways to apply CV systems, powered by CNNs, to real-world problems. It is clear that this trend is not likely to stop anytime soon.
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