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Image recognition typically is a process of the image processing, identifying people, patterns, logos, objects, places, colors, and shapes, the whole thing that can be sited in the image. And advanced image recognition, in this way, is a framework for employing AI and deep learning that can accomplish greater automation across identification processes.
As vision and speech are two crucial human interaction elements, data science is able to imitate these human tasks using computer vision and speech recognition technologies. Even it has already started emulating and has leveraged in different fields, particularly in e-commerce amongst sectors. Advancements in machine learning and the use of high bandwidth data services are fortifying the applications of image recognition.
So, how data science can assist in advanced image recognition?
Identifying Human Faces
Today, data science tools have started recognizing the human face and matching the face with all the pictures available in its database. Also, mobile phones equipped with cameras are leading to the creation of boundless digital images and videos. This huge amount of digital data is being utilized by companies to deliver better and convenient services to their customers. Typically, the facial recognition system of AI interprets all the features of a human face and compares it with its database to locate a match.
Data science tools with AI has the ability to not just assist users in face recognition but help detect objects available in the camera. The tools scan all the objects and attempt to name and identify them. Computers can make use of machine vision technologies in combination with a camera and AI software to achieve image recognition. Since image recognition is leveraged to act upon a large number of machine-based visual tasks, from labeling the content of images with meta-tags to performing image content search; visual search is one of the most advanced and eccentric tools of data science.
Beyond identifying faces and detecting objects in the images, data science is also capable of recognizing any special patterns, be it facial expressions or texture, in the image and matches it with its database. It also has the potential to detect colors and shapes present in the image and provides the users with appropriate insight into the contents of the image. Pattern recognition entirely relies on data and derives any outcome or model from data itself. It is the ability to detect arrangements of characteristics or data that yield information about a given image or dataset.
Applications of image recognition are ranging from smart photo libraries, targeted advertising and the interactivity of media, to accessibility for the visually impaired and enhanced research capabilities. Most tech giants like Google, Microsoft, Facebook, among others are pouring significant resources and researches into image recognition and related applications.
It is predicted that the global market of image recognition will reach from around US$16 billion to approximately US$39 billion by 2021.
This article has been published from a wire agency feed without modifications to the text. Only the headline has been changed.