Machine learning is an extensive branch that witnessed a lot of development in recent years. Computers are learning to think, act, and even see as a human. This seeing is, in other words, Computer Vision (CV). The message we see on various sites while logging in, i.e “verify that you’re a human”, is a part of machine learning and computer vision.
So, What Exactly is Computer Vision?
How exactly do we teach machines to perceive, analyze, and interpret images as humans do? Human eyes are quite complicated. We all have experienced this when we notice the difference between our own vision and that of a camera. In order to train computers to “understand” the image, scientists use methods of symbolic information disentangling through models of geometry, physics, statistics, and learning theory. Researchers are looking forward to constructing systems for image restoration, video tracking, scene reconstruction, etc. It is a sub-discipline of artificial intelligence and uses a lot of concepts from engineering and ML fields.
Tasks of CV
Recognition includes identifying objects, distinguishing persons, and detecting. Medical fields have a large application of detection, where the interpretation of information becomes more accurate. We can retrieve content-based images, train a robot to detect objects in any orientation, know the shape of an object, and identify text or characters with the help of Recognition.
Motion analysis is to determine the velocity of objects in an image, 2D or 3D, or of the camera. Determining the 3D motion of a camera with the help of image sequence, tracking the movements of an object, and optical flow are some of the applications of motion analysis.
Scene reconstruction is making a 3D model of a scene with the help of several 3D points. This does not require any algorithm of motion or scanning, which is making the process even faster. Further on this, grid-based 3D sensing gives us images from various angles. Certain algorithms bring these various pieces together to create a 3D model/scene.
Image restoration is nothing but making the image clearer or better. It removes noise or unnecessary blur using filters like low-pass or median. In other words, after determining a basic local structure of the image with the help of guiding lines and edges, these filters are applied.
Where Is Computer Vision Used?
A prominent field where CV plays a role is Healthcare; to process the medical images, and extract accurate data from them. It even helps in research to study organ structures and bodily changes. CV also facilitates quality control in Manufacturing, and in military executions. All the autonomous vehicles such as satellites, submersibles, and uncrewed vehicles use computer vision for better operation.
What Role Does CV Play in AI and ML?
In a nutshell, computer vision helps machines process and extract information from images or surroundings such as objects or persons. Various fields that are adopting AI automation have computer vision; agriculture, aviation, retail, etc use CV for monitoring the field/area in a better way. It is the base of automation where there is a high amount of visual data and it is difficult for a human to process it by oneself. For example, the work of security has gotten so much easier, as there is no need to have constant human surveillance. Computers can detect anomalies by themselves and let us know if there is any threat we need to attend to.
One of the basic AI features in our smartphones, facial recognition and smart cameras, use computer vision. CV is the best example of supervised machine learning, i.e training a machine to use data that is already reliable.
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