Advancements and Innovations in Tech world

3:21 AM RAWAT 0 Comments

All are aware of the fact and been very successful in terms of mimicking how a human captures light and color (our advanced cameras are on point), but this is just stage one. When one look outside, he immediately recognizes the objects in the yard. Without thinking, he is able to classify a dog as a dog, a tree as a tree, a dude as a dude - that’s his brain drawing knowledge from its extensive database. Considering computers, they see images as just huge piles of integer values that represent intensities across color spectrums; no context. And that’s where machine learning steps in.
Computer vision
concerns itself with building technology for obtaining information from images by processing through multi-dimensional data. It is a study of replicating the human vision processes, passing them on to machines and automating them. AI allows training a context for a data set in a way that enables a machine to understand what objects certain sequences of numbers actually represent. The type of ML algorithms one use in CV is called Convolutional Neural Networks (CNN) algorithms. What they are doing is break down pictures into small groups of pixels known as filters and afterward run calculations on them, comparing them against pixel matrices they already know about. CNNs determine such things as curves and rough edges, but, then several convolutions they are able to recognize objects such as animals, cars, and humans.
 When they begin, their filter values are randomized and so the predictions they produce are mostly false. But then they keep comparing its own predictions on labeled data sets to the actual ones, update they filter and improve accuracy with each iteration. CV is taking on progressively harder tasks and, in some cases, provides beautiful accuracy. It has a wide array of topics, logics and algorithms, each enabling some obvious applications, but also with new ideas and innovations.
Some of the popular computer vision applications:
§    Self-Driving Cars
§    Face Recognition
§    Image Search
§    Fingerprint Recognition
§    Iris Recognition (Identity Purposes)
§    Remote Sensing
§    Scene Construction
§    Editing of photos
§    Hawkeye (Used in sports)
Some applications to come in existence in the future along with some advancement in current applications:

§  Help Customers to Discover Products on basis of Visual Traits
§  Camera usage over the Keyboard to Search for Products
§  Scraping Data from Social Channels for Discovery
§  People Tracking for Non-Digital Ad Attribution
§  Gather data for Emotional Analytics and Tracking Consumer Attention
§  Usage of Visual Data for the Customer Personalization

While CV systems have a way to go in terms of accuracy, reliability, adoption, and potential privacy concerns, the progress and advancements in technology they have made over the past few years is outstanding enough to warrant the attention of marketers. Through this technology, marketers and business persons have the ability to explore the benefits and versatile use cases listed above, and much more. As the technology matures and society’s reliance on visual communication deepens, we can anticipate many new and exciting creative uses of CV to come.