Computer Vision: What and How?

Computer Vision (CV) gives the computers or machines the ability to see and understand. According to Wikipedia, it include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. It is a field of computer science or rather artificial intelligence (AI) that trains computers to obtain information from digital images or multi-dimensional data and take actions or make recommendations based on that information.

The advances in AI, neural networks and deep learning has made it possible to detect objects like cars, persons, trees, animals on the roads and label or classify them accordingly. As the technology has grown with advancing algorithms and powerful hardware, so has the accuracy rates for object identification.

History of Computer Vision

It was first conceptualized at some universities pioneering for AI in 1960s and the requirement was to mimic the human visual system, as a stepping stone to endow robots with intelligent behavior. Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, representation of objects as interconnections of smaller structures and motion estimation. Today, the computers and systems have reached to nearly 99% accuracy to detect and react to visual inputs.

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

How Computer Vision works?

CV works by training the computer to understand visual data that is fed into the systems and then subjecting it to different algorithms to recognize patterns and labeling them. The important parts of CV are:

Image classification: It is the fundamental task in CV which groups images into a specific class, label or category defined by a set of data points . The image classification accepts the given input images and produces output classification for analysis which is majorly done by deep neural networks.

Image segmentation: It is the process of fragmenting an image into multiple regions to be examined separately. Semantic segmentation and instance segmentation are among the core tasks of the technology. It teaches the computers to process an image at a pixel level and understand it.

Pattern recognition: It is the process of identifying patterns, regularities, shapes, colors and similar features in a visual data by examining an arbitrary set of figures. It attempts to assign each input value to one of a given set of classes and generates the correct output based on the instances.

Object detection: The process of identification of a specific object in an image with the help of advanced algorithms which can identify multiple objects like cars, vehicles, people or animals in an image of a street. Starting at the lowest levels of processing and working upwards, object detection joins the features together in a same way the human mind would do.

Feature Extraction: It is a type of pattern detection that matches similarities in images to help classify them. The network finds the patterns and breaks it down into distinct features, filtering out the patterns that are important by ignoring other portions of the image.

Computer Vision undoubtedly influences multiple industries from healthcare to manufacturing. It has gained popularity for expanding business and revenue-boosting opportunities.

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