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7 Applications of Computer Vision in AI

  • Ashesh Anand
  • Mar 16, 2022
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Human vision is similar to computer vision, with the exception that people have a head start. Human vision benefits from lifetimes of context to teach it how to distinguish objects apart, how far away they are, whether they are moving, and whether something is incorrect with an image. 


Computer vision teaches computers to execute similar tasks, but using cameras, data, and algorithms rather than retinas, optic nerves, and visual cortex, it must do it in a fraction of the time.


Computer Vision is a branch of computer science concerned with developing digital systems that can process, interpret, and comprehend visual input (pictures or videos) in the same way that people can. 


Computer vision is predicated on teaching computers how to interpret and understand images at the pixel level. Technically, machines use sophisticated software algorithms to retrieve visual input, process it, and interpret the results.


What is Computer Vision in AI and Machine Learning?


The method of perceiving images and films in digital representations is known as computer vision. Computer vision is used in Machine Learning (ML) and AI to train the model to detect particular patterns and store the data in its artificial memory, which can then be used to predict the results in real-life situations.


The goal of using computer vision technologies in machine learning and artificial intelligence is to construct a model that can work without human involvement. The entire process includes obtaining data, processing, analyzing, and comprehending digital images to use them in a real-world setting.


Also Read | How is AI being used for the benefit of Humanity?


Watch this: How Computer Vision Works

Human-level Performance of Computer Vision AI


Deep learning tasks are computationally intensive and costly, requiring a lot of processing power and large datasets to train models on. Deep learning methods, in contrast to traditional image processing, allow machines to learn on their own without the need for a developer to instruct them to recognize an image based on predetermined criteria. 


As a result, deep learning techniques have a high level of accuracy. Deep learning now allows machines to perform at human levels in image recognition tasks. For example, in deep facial recognition, AI models attain detection accuracy that is higher than that of humans (e.g., Google FaceNet achieved (99.63 percent)). 


Deep learning and computational vision have also attained human performance in classifying skin cancer at a level of competence comparable to dermatological professionals.


Computer Vision's Role in Artificial Intelligence


Computer vision as an applied discipline is spreading into a variety of fields. From AI research to machine learning, it plays a critical function in assisting machines in recognising various types of things in their natural surroundings.


Computer vision is the only technology that gives AI-enabled gadgets an edge to perform efficiently, from simple home tasks to identifying human faces, detecting things in autonomous vehicles, and battling opponents in a war.


Computer vision's application in artificial intelligence is spreading into new industries such as automotive, healthcare, retail, robotics, agriculture, autonomous flying such as drones, and manufacturing, among others.


Also Read | Common Architectures in Convolution Neural Networks (CNN)



Applications of AI in Computer Vision


  1. Object Recognization


This branch of computer vision AI is concerned with detecting one or more things in an image or video. Surveillance cameras, for example, intelligently recognize humans and their activities (no movement, things such as firearms or knives, etc.) so those suspicious activities are flagged.


  1. Image Segmentation


Image segmentation is a pixel-level computer vision technique for determining what is in a given image. It differs from image recognition, which labels a complete image with one or more labels, and object detection, which locates things inside an image by creating a bounding box around them. Image segmentation gives finer-grained information about an image's contents. 


  1. Images Categorisation


Image classification is the process of categorizing an image based on its surrounding visual content. The procedure entails concentrating on the relationships between neighboring pixels. A database with predetermined patterns makes up the classification system. 


These patterns are compared to the identified object to determine its classification. Vehicle navigation, biometry, video surveillance, biomedical imaging, and other fields all benefit from image classification.


  1. Real-time Augmentation


Augmented reality apps rely heavily on computer vision. This technology enables AR apps to detect physical things in real time (both surfaces and individual objects inside a physical location) and utilise that data to position virtual objects within the physical environment.



  1. Facial Identification


The goal of facial recognition technology is to recognize an item or a human face in a photograph. Because of the variety in human faces—expression, attitude, skin color, camera quality, position or orientation, image resolution, and so on—it is one of the more difficult applications of computer vision. 


This approach, however, is widely employed. It is used to authenticate users on smartphones. When Facebook suggests tags for people in a photo, it employs the same method.



  1. Recognize Patterns and Recognize Edges


A system's capacity to discover patterns of attributes or data is known as pattern recognition. A pattern can be a recurrent data sequence or a set of data that has been added to the system.


Finding the edges of objects within a picture is what edge detection is all about. This is accomplished by sensing brightness discontinuities. In data extraction and image segmentation, edge detection can be quite useful.



  1. Agriculture


Many agricultural companies use computer vision to monitor harvests and handle common agricultural issues like weed growth and nutrient deficit. Computer vision systems analyze photos from satellites, drones, and planes in order to discover problems early on, allowing for the avoidance of avoidable financial losses.


You can check out : How computers learn to recognize objects instantly | Joseph Redmon


How to Approach a Computer Vision Project


A computer vision application can be thought of as a tool for identifying jobs that need human visual skills and deducing a pattern from them. If a task can be automated, we can focus on designing a computer vision application.


Consider the following points while developing a computer vision application:


  1. Adapt Existing Occupations and Look for Modifications: We may design a computer vision-based solution by looking at existing jobs for inspiration, for example, computer vision can be used to detect vehicles that contravene traffic laws, read the number, and generate a fine slip for it. 


We can also check for existing applications that are having issues and provide a better solution for them.


  1. Research: At the end of the day, it'll all come down to research. When you're looking for inspiration, you can't avoid doing some research. The study will not only help you come up with fresh app ideas, but it will also allow you to investigate the market for existing apps.


  1. Brainstorm: We can gather problems and see if they can be solved with computer vision by brainstorming with our coworkers, friends, and family.


Also Read | Computer Vision Applications




Computer vision is utilized in a variety of businesses to improve the customer experience while lowering costs and increasing security. This technology differs from others in that it takes a distinct approach to data. 


Massive amounts of data that we generate on a daily basis, which some perceive as our generation's curse, are also used to our advantage: the data can educate computers to see and understand objects.


In the realm of AI, computer vision opens up a world of possibilities for consumers and enterprises. Self-driving automobiles, medical diagnostics, picture labeling, and cashier-less checkout are just a few of the applications of computer vision technology that demonstrate its versatility. 


This technology also represents a significant step forward in our civilization's efforts to develop artificial intelligence that is as intelligent as humans.