LyGuide Series: Computer Vision
What is computer vision? Is it just a fancy name for photography? Or is it like some kind of cybernetic version of the mind's eye? The answer is yes, and no. Computers can certainly do many things that humans can do visually, but what about the other senses? Can computers smell things too? Why does everything have to be about computers?! Let's explore these questions with an overview of what computer vision actually is.
The world is filled with a variety of visual data and it is becoming increasingly important to develop methods to understand and make sense of this data.
For example, consider the application of computer vision in autonomous vehicles (AVs). AVs use cameras to detect pedestrians in their path, which allows them to react quickly when an accident is imminent. In another example, consider how you can use your smartphone camera to scan QR codes at checkout counters or vending machines for payment purposes. Computer vision has also been used for medical imaging applications such as detecting tumors from CT scans or MRI images; this helps doctors identify problems earlier without having patients undergo unnecessary procedures like biopsies that could cause pain or discomfort if there are no issues present on further evaluation by doctors after diagnosis using techniques like X-rays."
You can use computer vision to do all kinds of things. For example, you could identify objects in images or classify them according to a set of criteria (e.g., "this is a dog"). You might also be able to recognize faces--and even tell if someone's smiling or frowning!
Computer vision can be used for tracking objects over time, understanding human body language (e.g., whether someone is pointing their finger), or even controlling autonomous vehicles like self-driving cars.
There are a number of challenges in computer vision. The first is that data is noisy, meaning that it contains errors and inconsistencies. Second, data may be unstructured or ill-defined; it doesn't have a clear structure or format that you can use to process it efficiently. Thirdly, there's the issue of volume: there's so much information out there that we don't even know what all of it looks like yet!
Fourthly (because this list is getting long), distributedness: all those images aren't just sitting on one computer somewhere--they're spread across millions of devices around the world at any given moment in time. Fifthly (and finally), dynamism; even if you manage to get hold of all those images somehow--or maybe even just one image--they're constantly changing over time due to factors such as lighting conditions and camera angle changes from one moment another so they won't always be consistent across multiple views taken under different conditions...
Computer vision is an important area of computer science that has many applications in the real world. It's used in robotics, autonomous vehicles, healthcare and more--and it's only going to become more relevant as AI grows in popularity. If you want to be at the forefront of this exciting field, now is a great time to get started!