Beyond the Human Eye: How Computer Vision Powers the Future

What is computer vision?

Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.  

       Unlike humans with their lifetime of visual experience, computer vision must rely on cameras, data, and algorithms to interpret the world. This is akin to a student cramming for an exam compared to someone with years of accumulated knowledge. However, computer vision has an advantage in its processing speed. By analyzing massive amounts of data at lightning speed, computer vision can outperform humans in specific tasks. Imagine a factory inspector meticulously examining each product for defects. A computer vision system, trained on countless images of good and bad products, can scan thousands per minute, identifying even the most subtle imperfections that might escape the human eye. In essence, computer vision condenses the learning process of human sight into powerful algorithms, excelling at specific applications where speed and precision are crucial.

How does computer vision work?

     Just like a human needs countless examples to learn and recognize objects, computer vision thrives on a vast amount of data. This data acts as the training ground, where the system repeatedly analyzes images to identify patterns and distinctions. To achieve this, consider training a computer to recognize car tires. It would require feeding it a massive dataset of tire images, including variations in size, brand, lighting, and even potential defects. By processing this data extensively, the computer vision system learns the intricacies of a tire, allowing it to accurately recognize them in real-world scenarios.

Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.

A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.

Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.

      Technology continues to evolve at a rapid pace, with huge leaps in the advancement of artificial intelligence (AI). These advancements open a world of new possibilities for the application of computer vision. This article will explore what lies ahead for computer vision trends in 2024. We will dive into what this will mean for the industry, the businesses that adopt it, and the wider society.

We will look at the following computer vision trends, use cases, and developments:

· Generative AI

· Multimodal AI

· Computer Vision in Healthcare

· Edge Computing and Lightweight Architectures

· Autonomous Vehicles

· Detecting Deep Fakes

· Augmented Reality

· Satellite Computer Vision

· 3D Computer Vision

· Ethical Computer Vision