Back to Blog

Computer Vision in Artificial Intelligence

Computer vision in AI

Computer vision in Artificial intelligence describes solutions that enable computers, software, or devices to analyze and categorize images. It gives machines the power to understand what they see and systematically arrange the data, giving them the capability to react to the information provided. Computer vision AI makes a machine view the world as humans.

The main components of computer vision are image recognition and detection. By replacing any visual activity, it makes processes faster and simpler. Unlike humans who get overwhelmed or biased, machines with the help of computer vision can see things in high detail and analyze them without any preconceived opinion. This brings quality improvements and a huge amount of time savings, leading to various industries pushing the limits with what computer vision AI can do.

With the advancement in deep learning and neural networks, there is now a fast-growing stream of useful applications obtained from computer vision. Few examples of these applications that we use everyday are:

Biometrics identification – Physical characteristics such as fingerprints, iris and facial identification are used for Biometrics identification

Enhancing Surveillance – An integral part of security, cameras at public places are used to detect suspicious behavior

Smart cars – Next-gen vehicles with computer vision AI can overcome driving obstacles, detect traffic lights, or road signs, in order to keep the passengers safe

Ebay App – Ebay app allows searching for items using your camera. You need to share an image with the mobile app, and it will show similar listings.

Facebook & Snapchat – Face detection algorithms apply filters and recognize you in pictures on Facebook and Snapchat

Computer-vision-AI-1

Image retrieval – Content-based queries are analyzed to search for appropriate images in Google Images which is an example of computer vision AI being used to search images in large databases.

Computer vision can be adapted by many industries in different ways. It is becoming increasingly common and you have most likely used products and services enhanced by computer vision AI. Here we discuss few of the major industries where computer vision applications are being applied.

Table of Contents

Healthcare

The use of computer vision AI in the healthcare sector has given rise to various applications that have increased the quality of healthcare across the world. Medical imaging has an important role in increasing a physician’s capacity to diagnose and take critical decisions towards the right treatment of diseases. Computer vision has the capability of offering precise diagnosis, which can potentially reach to the level of 100% accuracy. Computer vision does not miss out on details that can be possibly missed by human vision.

Computer Vision, owning to its pattern recognition capability, can diagnose diseases such as cancer in its early stages. Early detection of diseases is critical for its treatment and therefore in saving the life of patients.

Computer vision lessens the time doctors need in analyzing reports and images making them available to patients for personalized care. It leads to increased physician-patient interaction, giving them more time for patient consultation.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Retail

Computer vision AI has revolutionized the retail experience by allowing stores to monitor shoppers and automatically scan shopping carts while customers walk between the aisles. The software is being used in retail stores to make the customer shopping experience smoother and more personalized. An example of this is the Amazon Go concept from Amazon, which brings an end to checkout lines. The application detects, tracks and examines customer behavior in shops to automatically understand when they need to checkout and then sends them an electronic receipt.

A candy store in the U.S uses facial recognition to make personalized recommendations that are specific to every customer. When a client walks into the store, the sales associate addresses them by name and suggests new products based on their previous buys and preferences.

The application could also be used to analyze customer moods while they are moving back and forth between shelves. Facial recognition identifies their expressions and moods. Algorithms catch facial expressions in the video, processes the emotions, and interprets customer feelings towards products.

Banking

Computer vision has opened a lot of exciting opportunities in the banking sector which has been burdened by increasing paperwork and complicated processes. It has the capability of revolutionizing Banking from back-office operations to financial decision making.

One of the most important use cases of computer vision AI in Banking is KYC (Know Your Customer). The software allows banks to use biometrics for client identification purposes and to check the authenticity of documents such as driving license and other ID’s. The KYC process gets streamlined as customers can open accounts remotely over the phone. Some banks are now allowing customers to open accounts with selfies or video calls. The technology is helping in making the KYC process more convenient and customer-centric.

The software can help in creating a safe environment in banks by identifying the intent of individuals near cash points and detecting the threat. Customer behavior can be analyzed with emotion detection. Their micro expression, eye movements, and general behavior are analyzed to understand how banks can improve processes, offer personalized products, and make it more convenient for customers.

Computer vision can provide detailed high-level data to traders to help make trade decisions. It provides real-time analytics on industrial activities and movements that can help in identifying trends in the economy.

Industries like healthcare, retail and banking are finding new ways to implement computer vision AI to eliminate mistakes, save time and reduce costs. As the technology improves it is going to be adopted by more sectors to help make quicker decisions and provide efficient customer experiences.

Skyl.ai builds intuitive computer vision solutions that can help in a range of uses from content extraction, sentiment analysis, to image classification. Check out the unique business challenges we can solve with our computer vision machine learning models.

how-to-ensure-that-your-machine-learning-project-is-successful

    

Comments