COMPUTER VISION

-

 COMPUTER VISION



WHAT IS COMPUTER VISION :

Computer vision is a field of Artificial Intelligence (AI) that enables computers and systems to derive meaningful information from digital images , videos and other visual inputs - and take actions or make recommendations based on that information . If AI enables computers to think , computer vision enables them to see , observe and understand .

Computer vision works much the same as human vision , except humans have a head start . Human sight has the advantage of lifetimes of context to train how to tell objects apart , how far away they are , whether they are moving and whether there is something wrong in an image .

Computer vision trains machines to perform these functions , but it has to do it in much less time with cameras , data and algorithms rather than retinas , optic nerves and a visual cortex . Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute , noticing imperceptible defects or issues , it can quickly surpass human capabilities .

Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive - and the market is continuing to grow . It is expected to reach USD 48.6 billion by 2022 .


HOW DOES IT WORK :


Computer vision needs lots of data . It runs analyses of data over and over until it discerns distinctions and ultimately recognize images . For example , to train a computer to recognize automobile tires , it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire , especially one with no defects .

Two essential technologies are used to accomplish this : a type of machine learning called Deep Learning and a convolutional neural network ( CNN ) .

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data . If enough data is fed through the model , the computer will “ look ” at the data and teach itself to tell one image from another . Algorithms enable the machine to learn by itself , rather than someone programming it to recognize an image .

A CNN helps a machine learning or deep learning model “ look ” by breaking images down into pixels that are given tags or labels . It uses the labels to perform convolutions ( a mathematical operation on two functions to produce a third function ) and makes predictions about what it is “ seeing ” . The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true . It is then recognizing or seeing images in a way similar to humans .

Much like a human making out an image at a distance , a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions . A CNN is used to understand single images . A recurrent neural network ( RNN ) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another .


COMPUTER VISION APPLICATIONS :


There is a lot of research being done in the computer vision field , but it’s not just research . Real - world applications demonstrate how important computer vision is to endeavors in business , entertainment , transportation , healthcare and everyday life . A key driver for the growth of these applications is the flood of visual information flowing from smartphones , security systems , traffic cameras and other visually instrumented devices . This data could play a major role in operations across industries , but today goes unused . The information creates a test bed to train computer vision applications and a launchpad for them to become part of a range of human activities :

  • 1.) IBM used computer vision to create My Moments for the 2018 Masters golf tournament . IBM Watson watched hundreds of hours of Masters footage and could identify the sights ( and sounds ) of significant shots . It curated these key moments and delivered them to fans as personalized highlight reels .
  • 2.) Google Translate lets users point a smartphone camera at a sign in another language and almost immediately obtain a translation of the sign in their preferred language .
  • 3.) The development of self - driving vehicles relies on computer vision to make sense of the visual input from a car’s cameras and other sensors . It’s essential to identify other cars , traffic signs , lane markers , pedestrians , bicycles and all of the other visual information encountered on the road .
  • 4.) IBM is applying computer vision technology with partners like Verizon to bring intelligent AI to the edge , and to help automotive manufacturers identify quality defects before a vehicle leaves the factory .
EXAMPLES :


Many organizations don’t have the resources to fund computer vision labs and create deep learning models and neural networks . They may also lack the computing power required to process huge sets of visual data . Companies such as IBM are helping by offering computer vision software development services . These services deliver pre - built learning models available from the cloud - and also ease demand on computing resources . Users connect to the services through an application programming interface ( API ) and use them to develop computer vision applications .

IBM has also introduced a computer vision platform that addresses both developmental and computing resource concerns . IBM Maximo Visual Inspection includes tools that enable subject matter experts to label , train and deploy deep learning vision models - without coding or deep learning expertise . The vision models can be deployed in local data centers , the cloud and edge devices .

While it’s getting easier to obtain resources to develop computer vision applications , an important question to answer early on is : What exactly will these applications do ? Understanding and defining specific computer vision tasks can focus and validate projects and applications and make it easier to get started .

Here are a few examples of established computer vision tasks :

  • Image classification sees an image and can classify it ( a dog , an apple , a person’s face ) . More precisely , it is able to accurately predict that a given image belongs to a certain class . For example , a social media company might want to use it to automatically identify and segregate objectionable images uploaded by users .
  • Object detection can use image classification to identify a certain class of image and then detect and tabulate their appearance in an image or video . Examples include detecting damages on an assembly line or identifying machinery that requires maintenance .
  • Object tracking follows or tracks an object once it is detected . This task is often executed with images captured in sequence or real - time video feeds . Autonomous vehicles , for example , need to not only classify and detect objects such as pedestrians , other cars and road infrastructure , they need to track them in motion to avoid collisions and obey traffic laws .
  • Content-based image retrieval uses computer vision to browse , search and retrieve images from large data stores , based on the content of the images rather than metadata tags associated with them . This task can incorporate automatic image annotation that replaces manual image tagging . These tasks can be used for Digital Asset Management systems and can increase the accuracy of search and retrieval .
Written By - Ritesh Pandita  ©



Comments

Post a Comment

MORE LIKE THIS

BIG DATA ANALYTICS

-
Image
BIG  DATA  ANALYTICS WHAT IT IS AND WHY IT MATTERS Big data analytics examines large amount of data to uncover hidden patterns  , correlations and other insights . With today’s technology , it’s possible to analyse your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions . History and evolution of big data analytics : The concept of big data has been around for years ; most organizations now understand that if they capture all the data that streams into their businesses , they can apply analytics and get significant value from it . But even in the 1950s , decades before anyone uttered the term “ big data , ” businesses were using basic analytics ( essentially numbers in a spreadsheet that were manually examined ) to uncover insights and trends .  The new benefits that big data analytics brings to the table , however , are speed and efficiency . Whereas a few years ago...
GO DEEPER

REINFORCEMENT LEARNING

-
Image
     REINFORCEMENT  LEARNING What is reinforcement learning ? Reinforcement learning is a machine learning training method based on rewarding desired behaviors and / or punishing undesired ones . In general , a reinforcement learning agent is able to perceive and interpret its environment , take actions and learn through trial and error . How does reinforcement learning work ? In reinforcement learning , developers devise a method of rewarding desired behaviors and punishing negative behaviors . This method assigns positive values to the desired actions to encourage the agent and negative values to undesired behaviors . This programs the agent to seek long-term and maximum overall reward to achieve an optimal solution . These long - term goals help prevent the agent from stalling on lesser goals . With time , the agent learns to avoid the negative and seek the positive . This learning method has been adopted in artificial intelligence ( AI ) as a way...
GO DEEPER

ARTIFICIAL - INTELLIGENCE

-
Image
  ARTIFICIAL INTELLIGENCE   Introduction : Artificial intelligence is one of the advancements in the computer science , therefore can be said as a branch of computer science. It is the intelligence of machines. Generally, we do understand the intelligence of human beings, but when the same is depicted by machine, then termed to as artificial intelligence. The machine was supposed to perform any action on basis of the command given, but if the human-like thinking and analyzing, problem-solving capacity, voice recognition capacity in installed then the same proves it to be a smart one. Human intelligence is incubated by means of some processed inputs. There are many of the commands processed as input to the machines so that they give the desired results. Types of Artificial Intelligence : There are mainly two types of artificial intelligence. They are : Type 1 : Narrow Artificial Intelligence -  Can perform the single task example voice recognition. General Artificial I...
GO DEEPER