Computer Vision: Machine that recognizes you.
By: Camina Delacruz
Tags: Computer Science, Data Scientist, Computer Vision, Data Visualization, Artificial Intelligence

What is the meaning of computer vision?

It’s an interdisciplinary scientific topic that studies how computers can understand digital images or videos to a high degree. It aims to comprehend and automate operations that the human visual system can perform from an engineering standpoint. Methods for capturing, processing, analyzing, and understanding digital images and extracting high-dimensional data from the actual world to produce numerical or symbolic information, such as in choices, are all examples of computer vision tasks. In this application, understanding refers to converting visual images (retinal input) into descriptions of the environment that make sense to thought processes and evoke appropriate responses. The disentangling of symbolic information from visual data using models built with the help of geometry, physics, statistics, and learning theory can be considered image comprehension.

Larry Roberts is widely regarded as the father of computer vision, having studied the possibilities of extracting 3D geometrical information from 2D perspective views of blocks in his Ph. D. thesis (about 1960) at MIT (polyhedra).

What is the significance of computer vision?

We are inundated with photos today, ranging from selfies to landscapes. According to Internet Trends, people upload more than 1.8 billion photographs every day, and that’s just the number of uploaded images. Consider how much higher the figure would be if images kept on phones were included. Every day, we watch over 4,146,600 movies on YouTube and send 103,447,520 spam emails. Again, that’s only a portion of it; communication, media, and entertainment, as well as the internet of things, all play a role in this total. This abundance of visual content needs analysis and comprehension by teaching machines to “see” these images and videos, computer vision aids in this process.

Furthermore, today’s internet is easily available to everyone because of improved connectivity. Children are particularly vulnerable to internet toxicity and abuse. Computer vision automates various activities, but it also ensures the moderation and monitoring of online visual information. Indexing is one of the most important aspects of online material curation. Because most internet information falls into two categories: text, visual, or audio, categorization is easy. Algorithms are used in computer vision to read and index images. Computer vision is used by popular search engines like Google and Youtube to approve photos and videos for inclusion. They not only supply consumers with relevant content, but they also safeguard them against online harassment and “toxicity.”

The evolution of computer vision

For nearly 60 years, scientists and engineers have been attempting to develop ways for robots to see and analyze visual input. In 1959, neurophysiologists presented a cat with a series of images to see if it could correlate a response in its brain. They noticed that it initially responded to hard edges or lines, implying that image processing begins with simple shapes such as straight edges.

The first computer image scanning technology was created simultaneously, allowing computers to digitize and acquire images. In 1963, computers were able to convert two-dimensional images into three-dimensional forms, marking yet another milestone. AI became an academic topic of research in the 1960s, and it was also the start of AI’s attempt to address the human vision problem.

Optical character recognition (OCR) technology was introduced in 1974, and it could recognize text printed in any typeface or typeface. Similarly, neural networks could be used to decipher handwritten text using intelligent character recognition (ICR).

OCR and ICR have since made their way into document and invoice processing, vehicle plate recognition, mobile payments, machine translation, and various other applications.

David Marr, a neuroscientist, discovered that vision was hierarchical in 1982 and developed algorithms for robots to detect edges, corners, curves, and other basic structures. Kunihiko Fukushima, a computer scientist, devised a network of cells that could recognize patterns simultaneously. Convolutional layers in a neural network were used in the Noncognition network.

By the year 2000, the focus of the research had shifted to object recognition, and the first real-time face recognition applications had developed by 2001. Throughout the 2000s, standardization of how visual data sets are labeled and annotated arose. The ImageNet data set was released in 2010. It comprised millions of annotated photos from a thousand different object classes and served as the basis for today’s CNNs and deep learning models. A team from the University of Toronto back in 2012 competed in an image recognition competition with a CNN. The model, dubbed AlexNet, significantly reduced picture identification error rates. Error rates have dropped to just a few percent since this breakthrough.

How to learn Computer Vision?

Computer vision can be used in different ways, including:

1. Laying the Groundwork: Getting into the area requires an understanding of probability, statistics, linear algebra, calculus, and basic statistics. Similarly, familiarity with programming languages such as Python and MATLAB will aid your understanding of the ideas.

2. Digital Image Processing: Discover how to use JPEG and MPEG files to compress photos and films. Basic image processing technologies such as histogram equalization, median filtering, and others are required. Once you’ve mastered the basics of image processing and restoration, you’ll be ready to learn more advanced computer vision techniques.

3.Machine Learning Basics: To get started with computer vision, you must understand Convoluted Neural Networks, Fully Connected Neural Networks, Support Vector Machines, Recurrent Neural Networks, Generative Adversarial Networks, and Autoencoders.

4.Essential Computer Vision: The next stage is to decode the mathematical models used during image and video formulations. You can on to advanced study once you grasp how pattern recognition and signal processing work.

Computer Vision Applications

Medical Imaging: Computer vision aids MRI reconstruction, automatic pathology, diagnostics, and machine-assisted procedures, among other applications. Object occlusion (dense depth estimation), outside-in tracking, and inside-out tracking for virtual and augmented reality are all examples of AR/VR.

Smartphones: Computer vision applications include photo filters (including social media animation filters), QR code scanners, panorama creation, computational photography, face detectors, and image detectors (Google Lens, Night Sight). Image search, geolocation, image captioning, aerial imaging for maps, video categorization, and more are available on the internet.