Do you ever wonder what happens when you take a picture? How does your phone or computer know how to display that image on the screen? The answer is computer vision. Computer vision is the process of understanding and extracting information from digital images and videos. It is a field of research that has been around for decades but has only recently become popular due to technological advancements in computer hardware and software. This blog post will discuss the history of computer vision and the technical aspects of how it works.
What Is Computer Vision And What Are Its Uses?
Computer vision is a field of artificial intelligence that deals with how computers interpret and understand digital images. Just like human eyes, computer vision systems rely on a combination of hardware and software to analyse images and extract meaning from them. However, computer vision takes things a step further by using machine learning algorithms to improve the accuracy of its image interpretation over time automatically.
There are countless potential uses for computer vision, ranging from retail applications like automatic product sorting to medical applications like automated cancer detection. In general, computer vision can be used whenever it would be helpful to have a machine analyse and interpret digital images. As technology continues to develop, you can expect to see even more innovative and impactful uses for computer vision in the future.
The Technological Advancements That Enabled Computer Vision
Computer vision is not a relatively new field, but it has been made possible only due to recent technological advancements. To understand computer vision, you need to look back at the history of computer technology. In the early days, computers lacked the power and memory to analyse digital images meaningfully. But as computer hardware and software improved over time, computer vision became feasible.
One of the most significant steps forward for computer vision came from developing deep learning algorithms. These algorithms allowed computer systems to “learn” from data by automatically adjusting parameters within their software. This enabled computer systems to become more accurate in interpreting images without needing direct programming instructions from a human.
Developing powerful computer hardware to support deep learning algorithms also enabled computer vision. GPU (Graphics Processing Unit) processing, in particular, has made computer vision much more efficient and cost-effective. GPUs are specialized computer chips designed to quickly process large amounts of data, making them ideal for computer vision applications.
How Does Computer Vision Work?
Computer vision teaches computers how to interpret and understand digital images. In other words, it’s all about teaching computers to see. To understand how computer vision works, it is best to know what its main component does.
Computer vision needs hardware to capture analogue images and convert them into digital form. This is done with the help of computer hardware such as cameras, image sensors, and computer vision chips.
The software part of computer vision plays a significant role in analysing and interpreting the images captured by the hardware. It is responsible for breaking down an image into its components, like colours and objects, which can then be analysed further. This analysis is done through machine learning algorithms specifically designed for computer vision applications. These algorithms allow computers to recognise patterns in images and make sense of what they see. For example, deep learning algorithms have made it possible for computers to see images of human cells better for medical diagnosis.
Computer vision is possible by following these processes with the components in place.
The computer vision system begins with the image capture module. This is where hardware like cameras and image sensors take an analogue image and convert it into a digital form that can be analysed by computer software. The better the image quality and resolution, the more information can be extracted.
Once the image has been captured in digital form, computer vision systems must process it for further analysis. This involves filtering out any noise or distortions in the image so that the computer can focus on the relevant features of the scene. This step is essential for computer vision because it helps reduce complexity and makes interpretation easier later.
After pre-processing, computer vision systems use algorithms to detect essential features in an image. These features could include line segments, shapes, object contours, colour patterns, etc. This step is crucial for computer vision because it allows the computer to identify objects in an image and extract meaningful information.
The final stage of computer vision is object recognition. This step involves using algorithms to identify objects in an image based on their features. The computer can then classify the objects according to their specific characteristics, allowing a more accurate interpretation of what’s being seen in the scene.
Computer vision has come a long way since its inception, and you will continue to see even more innovative and impactful uses for computer vision in the future. Its development has been made possible due to technical advancements like GPUs, deep learning algorithms, and computer hardware, which are key to computer vision’s success. With its many applications in fields such as medical imaging, facial recognition, and autonomous vehicles, computer vision is an essential tool for many industries.