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Table of contents
1.
Introduction
2.
Understanding Computer Vision
3.
Applications of Computer vision
4.
FAQs
5.
Key takeaways
Last Updated: Mar 27, 2024
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Applications of Computer Vision

Author Md Yawar
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Introduction

Source: link

When you look at the above image, you can see people celebrating something. Perhaps some festival? You can see the crackers and people enjoying and filming them. Ahh! this is Diwali, one of the biggest festivals in India.

We can see images like these and understand what they are about. Visualizing is one of the most important aspects of our lives. But, to a computer, this image is nothing more than a matrix of red, green, and blue pixels.

For years, no one ever thought computers could understand visual data such as images. But, after the rise of new technologies such as machine learning and artificial intelligence. Computer vision is no longer a dream. Computers can now see and understand an image, make useful predictions, and extract information. Let us look at what computer vision is and how it is used in everyday life.

Understanding Computer Vision

                                                 

Source: Link

Computer vision (abbreviated as CV) is the field of computer science that focuses on making computers see and understand images. It uses various algorithms to learn from the training data that usually include thousands or even millions of images. Convolutional Neural Network is one of the leading technologies for image classification and prediction.

“Computers can see, hear, and learn. Welcome to the future”

– Dave Waters

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Applications of Computer vision

Computer vision is used in many industries. It is very efficient and saves us a lot of time in doing cumbersome tasks such as defect detection. We unknowingly use computer vision every day when we unlock our phones using face detection or search for something on the internet using images.

Computer vision is a mature and reliable technology, and we use it almost everywhere. Some of the applications of computer vision are:

Healthcare:

Computer vision is widely used in the healthcare industry. There is a lot of image-based data in the healthcare industry. 

Some of the applications of computer vision in the healthcare industry are:

  • Diagnoses, control, and treatment of Covid-19
  • Cancer detection
  • Tumor detection
  • Diagnoses of diseases
  • Remote monitoring of the patients
  • Improved medical analysis by creating accurate visuals of the organs
  • Predicting diseases beforehand
     

 

computer vision diagnose pneumonia from an X-Ray image 

Defect detection:

It is one of the most common use cases of computer vision. In any large-scale production, defective pieces are commonly generated. Sometimes, unwanted objects such as impurities come into the production line. Quality checks by humans in such large productions will greatly reduce the production time. With computer vision, we can detect any defects in the production in no time, which is not possible by humans.

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Image Transformation:

With the rise of the internet, the processing and sharing of images have become a widespread thing. We tend to process the images while sharing them on the internet. Computer vision can transform our pictures in virtually any way we want. Below is an example of changing an image using a computer vision technology known as GAN.

                           

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Hiring process:

With companies getting so many applications, selecting good candidates is a cumbersome task. It takes a lot of time and effort to shortlist appropriate candidates. Computer vision can be used to assess the candidates based on their soft skills or communication skills and provide early screening of the candidates.

                                  

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Agriculture:

Agriculture has seen a lot of contribution from AI technologies. Precision agriculture is slowly replacing traditional agriculture to maximize the output from our fields. This is possible due to various new technologies such as Computer vision. Computer vision is used in crop monitoring, predicting which part of the field can grow which crop, livestock management, etc.

                      

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Automotive:

Computer vision is used in the production of the automotive industry. It can detect flaws that the human eye cannot even see. It is also used in monitoring to ensure every part is working properly. 

Computer vision has also made self-driving cars possible, which was not possible before.

                                 

Source

Oil and natural gas:

This industry requires a precise location for the digging and extraction of oil. Computer vision can scan thousands of images and sort the desired locations within seconds. 
 

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Social distancing tools:

Social distancing is one of the main precautions from the Covid-19. People sometimes do not follow the social distancing guidelines. To ensure proper social distancing, we can create social distancing tools that can detect the violations in social distancing and ensure actions to be taken on the same.

 

                                     

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Military: 

Computer vision is used in guided missiles and the detection of enemy troops. Accurate battlefield analysis can be done with the help of computer vision. Automatic drones use computer vision to navigate enemy bases through challenging terrains.

                                                          

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Also see, Sampling and Quantization

FAQs

  1. What are some other applications of computer vision?
    Computer vision is also used in construction, video surveillance, etc. 
     
  2. How does computer vision work?
    The basic intuition behind computer vision is taking images as input, processing them using some algorithm, and getting the output.
     
  3. What is OpenCV?
    OpenCV is a library in python used to solve computer vision problems.

Key takeaways

In this article, we looked into computer vision and its applications. We saw how computer vision is used in various industries. There are more applications of computer vision. 

To know more and read interesting blogs on machine learning check out Machine Learning Blogs.

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