The term “computer vision” refers to a technique that uses artificial intelligence (AI) to enable computers to extract useful information from visual inputs. Following that, automated actions are taken using the insights gleaned from computer vision. This page explains computer vision in detail, including its definition, uses, and examples.
How is computer vision implemented?
The system needs to be educated on hundreds of thousands of examples in order to recognize visual objects. You could want someone to be able to tell an automobile from a bicycle, for instance. How would you explain this job to a person?
Typically, you would remark that a machine has four wheels while a bicycle has two. or that the machine lacks pedals whereas a bicycle does. This is referred to as feature engineering in machine learning.
5 Examples of Computer Vision
The following five computer vision examples highlight the potential of this AI-powered approach to.
1. Google Translate
Google, a pioneer in technology, launched its quick translation service in 2015, utilizing computer vision via smartphone cameras. In 2016, Google Translate online results included Neural Machine Translation, a crucial technique that powers quick and precise computer vision-based translation.
When the app is opened on a camera-equipped internet-capable smartphone, the cameras pick up any text in the surrounding environment. The program then recognizes the text automatically and translates it into the user’s chosen language. For instance, a person can use their smartphone’s camera to point at a billboard or poster with text written in a different language and read it on the screen in their preferred language.
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2. Facebook 3D Photo
Technology behemoth Meta, formerly known as Facebook, is also experimenting with machine vision for a variety of intriguing uses. One such application is the creation of 3D models from 2D images.
Facebook 3D Photo, which was introduced in 2018, at first needed a smartphone with two cameras in order to produce 3D images and a depth map. Although this initially restricted the use of this feature, the widespread availability of reasonably priced dual-camera phones has recently enhanced its popularity.
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The pre-trained object identification model YOLO, which stands for You Only Look Once, makes use of transfer learning. It has a variety of uses, including enforcing rules regarding social distance. The YOLO algorithm can instantly find and identify items in a visual input as a computer vision solution. Convolutional neural networks, which can simultaneously forecast various bounding boxes and class probabilities, are used to do this.
Faceapp is a well-known image-editing program that alters the gender, age, and other attributes of visual inputs of human faces. Deep convolutional generative adversarial networks, a particular branch of computer vision, are used to do this. Faceapp uses deep learning and image recognition methods to identify prominent face characteristics like cheekbones, eyelids, the bridge of the nose, and jawline. The program can alter these traits to change the appearance of the image after they are defined on the human face.
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Sentio created SentioScope, a method for tracking athletic activity. It primarily functions as a soccer player tracking solution, analyzing in-game video inputs in real-time. Recorded data is uploaded to analytical cloud-based platforms. A 4K camera system is used by SentioScope to record visual inputs. After processing these data, it can identify players and derive real-time insights from their behavior and movement.
This computer vision-based approach builds a two-dimensional conceptual model of the soccer field to represent the game. A thick spatial cell grid is used to divide up this 2D model. As shown in the movie as a fixed image patch, each cell represents a different ground point on the field.