What is Computer Vision? Computer vision has been around for more than 50 years, but recently, we see a major resurgence of interest in how machines ‘see’ and how computer vision can be used to build products for consumers and businesses. Few examples of such applications are- Amazon Go, Google Lens, Autonomous Vehicles, Face Recognition. The key driving factor behind all these is Computer Vision. In the simplest terms, Computer Vision is the discipline under a broad area of Artificial Intelligence which teaches machines to see. Its goal is to extract meaning from pixels. From the biological science point of view, its aims are to come up with computational models of the human visual system. From the engineering point of view, computer vision aims to build autonomous systems which could perform some of the tasks which the human visual system can perform (and even surpass it in many cases). A brief history n the summer of the year 1966, Seymour Papert and Marvin Minsky at MIT Artificial Intelligence group started a project titled Summer Vision Project. The aim of the project was to build a system that can analyze a scene and identify objects in the scene. So the vast, puzzling area of computer vision that researchers and tech giants are still trying to decode was first thought to be simple enough for an undergraduate summer project by the very people who pioneered artificial intelligence. In the 70s, taking ideas from studies of the cerebellum, hippocampus and cortex for human perception, David Marr, a neuroscientist at MIT, set up the building blocks for the modern Computer Vision and thus is known as the father of the modern Computer Vision. Majority of his thoughts are culminated in the major book simply titled VISION. Deep Vision Deep Learning has taken off since 2012. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Powering recommender systems, identify and tags friends in photos, translate your voice to text, translate text into different languages, Deep Learning has transformed Computer vision leading towards superior performance.
Applications
Smartphones: QR codes, computational photography (Android Lens Blur, iPhone Portrait Mode), panorama construction (Google Photo Spheres), face detection, expression detection (smile), Snapchat filters (face tracking), Google Lens, Night Sight (Pixel) Web: Image search, Google photos (face recognition, object recognition, scene recognition, geolocalization from vision), Facebook (image captioning), Google maps aerial imaging (image stitching), YouTube (content categorization) VR/AR: Outside-in tracking (HTC VIVE), inside out tracking (simultaneous localization and mapping, HoloLens), object occlusion (dense depth estimation) Medical imaging: CAT / MRI reconstruction, assisted diagnosis, automatic pathology, connectomics, AI-guided surgery
Future of Computer Vision As per a report, Computer Vision market was valued at 2.37 billion U.S. dollars in 2017, and it is expected to reach 25.32 billion U.S. dollars by 2023, at a CAGR of 47.54%. The world is undergoing a deep digital transformation, especially India that shows no signs of slow down. Average monthly data consumption of Jio alone is 10.8 GB. According to this report, Every Minute- Users watch 4,146,600 YouTube videos Instagram users post 46,740 photos Snapchat users share 527,760 photos Which all give a huge set of opportunities to computer vision find patterns and make sense of it. Even with all the fascinating developments, AI and the area of computer vision specifically need to tackle problems associated with it currently such as bias, risk unawareness and lack of explainability. To tackle such problems, companies like Ping An has started taking baby steps, utilizing Symbolic AI, an early form of AI, into modern AI algorithms to give explainability of its decision but there is still a way to go.
Post a Comment