The latest advances in Computer Vision

When we talk about revolutions that have shaped the way computers work, computer vision definitely comes to the forefront. Virtual reality, home tech synchronizing, and mobile payments are some of the most common applications of computer vision that have rapidly gained traction. And given that it can be amalgamated with different technologies, the future of computer vision seems bright and is likely to dominate the markets in 2022 and beyond. In this article, we are going to take a closer look at the latest advances in computer vision solutions. We are going to talk about the future of computer vision as well.

Computers seem like a pretty modern invention, but their history dates back to the early 19th century. Since then, there have been several significant shifts and revolutions in the way we use them. Computer vision is undoubtedly one of them. Thanks to this AI-related technology, we have access to a whole range of new applications and services that were unimaginable back in the 19th or 20th century.

Read on as we give you a comprehensive overview of the latest advances in computer vision that will dominate the world in 2022. But before we get to that, let’s begin by understanding what computer vision is all about.

What is Computer Vision?

Computer vision is one of the subfields of artificial intelligence that focuses on creating digital systems that can derive meaningful information from videos, digital images, and other visual inputs similarly to humans. This fantastic AI-related invention is all about understanding and automating tasks that are usually done by the human optical system. This includes correctly identifying and observing objects or people in a digital image and providing appropriate output.

Here are common tasks that a computer vision system can perform:

• Object Identification: Computer vision systems go through visual content to identify particular objects on a video or photo.
• Object Classification: The system parses information to classify objects according to the defined category.
• Object Tracking: The system goes through videos or images to track objects according to a given search criterion.
• Content-based image retrieval: The system browses, searches, and retrieves images from large data stores.

Computer vision works pretty much the same as human vision. The only difference is that humans have the advantage of contextual experiences that enables them to tell objects apart, whether they are moving, how far away they are, and if something’s not right with the image. On the other hand, computer vision relies on artificial intelligence to accurately identify and interpret digital images, videos, and deep learning models.

With that introduction done, let’s see how computer vision is exploited in the modern technological and business world. We are also going to talk a bit about the future of computer vision as well.

It might be interesting for you: Computer Vision Case Study: Image Generation Process (Step-By-Step)

Current Trends in Computer Vision

Below, we list the top 3 advances in computer vision that you should know about. We reckon then will have a tremendous impact on the development of AI and related technologies.

Deep Learning in Image Recognition and Classification

Deep learning has gained popularity mainly because of its capability to deliver accurate results. Researchers can now use this technology to solve complex computer vision tasks such as image recognition and image classification.
In a nutshell, deep learning is a subset of machine learning that teaches computers to do what humans can do. Its architecture is influenced by the structure of the human brain, known as artificial neural networks. And since most deep learning techniques utilize neural network architectures, deep learning models are often referred to as deep neural networks.

Deep neural networks are the future of computer vision. They are designed to recognize different patterns by learning their individual features. The conventional approach of image recognition in computer vision uses a sequence of image filtering, feature extraction, and rule-based classification techniques, which require a lot of engineering time and a high level of expertise.

But with deep learning, image recognition is made easier by the use of algorithms that recognize hidden patterns from a set of bad and good data samples. When combined with AI hardware and GPUs, deep learning can achieve real-time and above human-level of image detection.

Microsoft’s Project Adam[1] is a perfect example of how deep learning can be used in image recognition. Project Adam is an advanced computer vision program used to identify particular dog breeds in images, a task that’s too difficult for a human vision system to perform. It uses the world’s best photograph classifier to recognize images even in different environmental settings. The classifier uses 14 million images from ImageNet, split up into 22,000 categories. Besides its excellent speed and accuracy, Project Adam is highly scalable–something that Google still falls short of.

Source: microsoft.com

Edge Computing

We live in a technological era where business executives make time-critical business decisions based on what they see on computer vision systems. Often, it takes too long for images and videos to be sent to a centralized location, making it harder to make the necessary decisions within the speculated timelines. And sometimes, physical network limitations don’t provide enough bandwidth for all the videos and images to be sent to the same location.

Without a doubt, computer edge computing is one of the reasons why computer vision can rapidly develop today. With this technology, we can process images or videos near the data source rather than rely on cloud networks far from the data access points. This is quite instrumental in tapping into the infinite possibilities of what computer vision can achieve. It addresses bandwidth and latency issues and offers more privacy than the cloud. Edge computing can’t really replace cloud computing because there’s a likelihood that we’re going to need centralized processing for some time. But it’s here to cover for its shortcomings.

This technology is especially widely used in the healthcare sector. Healthcare providers use it in conjunction with other technologies to convert data into new insights that can be used to improve patient outcomes while delivering operational and financial value. One of the latest advancements is the use of edge computing in computer vision to assist the visually impaired. It can help with:

• Object identification
• Obstacle detection
• Recognizing people
• Sign detection and navigation, among others.

The third trend that we want to tell you about is especially exciting, and it has the potential to revolutionize many B2C sectors, including e-commerce and marketing.

Merged Reality: VR and AR Enhanced

Augmented reality (AR) and virtual reality (VR) give users an interactive experience of a real-world environment enhanced by computer-generated information. They have become prevalent across a broad range of business applications, particularly in the education, marketing, gaming, and e-commerce sectors. The two terms are sometimes used interchangeably, but they are quite different from each other.

• Augmented Reality: Uses real-world settings, and users can control their presence in the setting. It can be accessed by a smartphone.
• Virtual Reality: It’s completely virtual and enhances a fictional reality. And unlike AR, users are controlled by the system and require a headset device to operate.

These technologies are going to get even bigger in the future. But issues like presentation, control, and precise sensing are bound to curtail its growth. This is where computer vision comes in. It has driven VR and AR into the next developmental stage known as merged reality (MR).

Unlike AR and VR, merged reality allows you to interact with virtual worlds via regular gestures. This minimizes the side effects that come from using AR and VR, such as headaches. With the help of sensors and external cameras that map the environment, and tracking solutions to put the user in the right position, VR and AR systems can perform activities such as:

• Providing the user with guidance and directions around indoor environments and public spaces
• Detecting the user’s eye and body movement and guiding them around obstacles in the VR environment.

These immersive experiences are instrumental to the future of computer vision. They provide a safe environment for different activities such as learning, shopping, and conducting experiments. One of the most common real-life applications of MR is in the education sector. For example, students can use MR 3D simulations to interact with them and even create and manipulate virtual objects. This makes it easier for them to study the objects in a way that’s relevant for them and their studies.

Final Thoughts on Computer Vision

When it comes to computer vision, there is surely a bright future ahead. Its applications are slowly emerging into different commercial and industrial sectors, especially in the post-Covid hyper-digital industrial era. New trends such as merged reality, edge computing, and image recognition using deep learning techniques are opening a wide range of possibilities for the application of computer vision in real-life. Clearly, computer vision is here to stay and will dominate our digital spaces throughout the year.

If you want to find out more about this fascinating technology, check our computer vision solutions.

The Addepto team is eager to show you how this technology could work in your organization. Contact us