Improving Customer Experience with Computer Vision Applications

Abhinav S | 11th July 2019

The proliferation of artificial intelligence in our daily lives is increasing steadily. It is mostly due to leading consumer companies who use computer vision, recommender systems, and natural language processing to improve customer experience. Companies are making significant investments in smart computer vision app development to serve customers better and gain a competitive edge. Computer vision is automating several processes in travel, finance, healthcare, and other industries.

This blog post explores the impact of computer vision applications with real-life examples.

Computer vision applications create their own market

The idea of computer vision first came to light in the 1970s, but it could not materialize due to lack of suitable technology. However, hardware and software advancements over the years have made computer vision a reality with applications in multiple industries. Researchers are discovering new computer vision applications to replace human abilities, improve operations, and pursue new opportunities.

Leading research firm Morgan Stanley has revealed that Snapchat, WhatsApp, Facebook, FB messenger, and Instagram users share 3 billion images daily. It makes a fitting case for computer vision systems that can analyze them to guide the decisions of marketers and shape business strategies.

The analysis of digital images or videos with machine learning algorithms and deep learning saves time. Computers can learn to identify objects, animals and people in images with reinforcement learning techniques by scanning a large image repository. The results they provide have a high level of accuracy.

How do they work?

Computer vision applications use neural networks, which are computer systems that mimic the ability of humans to identify patterns. Convolutional neural networks (CNN) is the most common neural network for identifying images. Computer vision researchers use CNN because it requires very little programming. The application of CNN ranges from identifying faces, objects, and traffic signs to empowering vision in robots and autonomous cars.

Fast response and accuracy make CNN an ideal tool for image and object recognition. Studies have shown it to be as accurate as humans in recognizing object and images. For example, CNN can easily identify blurred photos and images using filters. Also, when it comes to fine-grained details, like identifying the breed of a dog, humans can fail, but CNN does it with ease. It further highlights how effective it is at recognizing objects and images.CNN enables the development of feature-rich applications for image and video recognition, recommender systems, medical imaging analysis, and natural language processing.

Deep learning has further enhanced computer vision capabilities for developing better business applications. Majority of computer vision systems use visible-light cameras to scan images at 60 frames per second (fps) or slower. A few of them use image-acquisition hardware with active illumination or something other than visible light or both. Thermographic sensors, radar imaging, and magnetic resonance images work on this principle. The images captured by the hardware is then processed using the same computer vision algorithms used to process visible-light images.

Computer vision applications

The most widely used application of computer vision is facial recognition. A June 2016 study estimated that by 2022, the global facial recognition market would generate $9.6 billion of revenue. The estimates showed a compound annual growth rate (CAGR) of 21.3% over the period 2016-2022.
Government administration accounts for the highest use of facial recognition technology mainly for security purposes.

Following are the top 3 industries where facial recognition is playing an important role:

1. Security:- Facial recognition technology is effective at solving crimes, detecting faces of perpetrators, scanning documents to detect counterfeit ones. Government administrations use computer vision systems to scan passports and match faces during border checks.

Smartphone manufacturers use computer vision to make facial biometrics work as a security mechanism for unlocking phones, accessing applications and signing in. Software giants like Apple have promoted facial identification features in their smartphones.

2. Health:- There are significant benefits of using computer vision in healthcare. With the aid of face analysis and deep learning, it is now possible to –
a) Track a patient’s medication history and the effectiveness of medicines
b) Detect genetic diseases such as DiGeorge syndrome
c) Support pain management procedures

Along with these, AI is speeding up the process of interaction between doctors and their patients.

3. Retail:- Identifying customers is essential to provide them a personalized experience. Leading retailers use cameras enabled with computer vision to analyze customer’s behavior while shopping and improve the purchase process.

Amazon GO actively uses computer vision systems in their stores where shoppers can walk in, pick items, and walk out of the store without having to wait in a queue. The store uses a computer vision system to identify customers and track items picked up by them to charge them automatically. Chinese tech and retail giant, Alibaba too, is testing facial recognition systems to streamline the shopping and payments experience of their customers.

Other computer vision applications are object recognition, machine vision, augmented reality (AR), and self-driving cars. Deep learning algorithms are at the core of these applications.

Players in the food & packaging industry too, use machine vision for error-free assembly operations. It is effective at inspecting closure panels such as doors, hoods, lift gates, and tailgates, among other components. The interactive user interface of machine vision technology enables system operators to do quality inspection easily with minimal labor training.

Several companies are implementing machine vision to perform grading, sorting, portioning, processing, and quality checking during processing and packaging.

Future scope of computer vision applications

Market trends and advancements in artificial intelligence depict a clear growth in the usage of computer vision applications. Facial recognition for security purposes, machine vision for quality inspection, and logo detection to identify competitors are critical success factors for consumer brands.

Research and development are underway to launch self-driving cars on roads. Computer vision is playing a significant role in this futuristic technology. Companies like Tesla and Waymo are leading the way to make autonomous cars a regular sight for humans.

Oodles provides AI app development services

We have a team to develop AI solutions for businesses to let them improve their end-user experience. We have experience in solving industry-specific challenges. Our AI team has developed an AI toolkit to accelerate the development of AI solutions including computer vision systems.

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Abhinav S

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