Introduction to Computer Vision

One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more. Object detection is probably the most profound aspect of computer vision due the number practical use cases.

Multi-Object Detection and Classification

Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity.

Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.

One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better.

How Does Computer Vision Work?

One of the major open questions in both Neuroscience and Machine Learning is: How exactly do our brains work, and how can we approximate that with our own algorithms? The reality is that there are very few working and comprehensive theories of brain computation; so despite the fact that Neural Nets are supposed to “mimic the way the brain works,” nobody is quite sure if that’s actually true.

The same paradox holds true for computer vision — since we’re not decided on how the brain and eyes process images, it’s difficult to say how well the algorithms used in production approximate our own internal mental processes. On a certain level Computer vision is all about pattern recognition. So one way to train a computer how to understand visual data is to feed it images, lots of images thousands, millions if possible that have been labeled, and then subject those to various software techniques, or algorithms, that allow the computer to hunt down patterns in all the elements that relate to those labels.

So, for example, if you feed a computer a million images of cats (we all love them😄😹), it will subject them all to algorithms that let them analyze the colors in the photo, the shapes, the distances between the shapes, where objects border each other, and so on, so that it identifies a profile of what “cat” means. When it’s finished, the computer will (in theory) be able to use its experience if fed other unlabeled images to find the ones that are of cat.

Let’s leave our fluffy cat friends for a moment on the side and let’s get more technical🤔😹. Below is a simple illustration of the grayscale image buffer which stores our image of Abraham Lincoln. Each pixel’s brightness is represented by a single 8-bit number, whose range is from 0 (black) to 255 (white):

At left, our image of Lincoln; at center, the pixels labeled with numbers from 0–255, representing their brightness; and at right, these numbers by themselves.

This way of storing image data may run counter to your expectations, since the data certainly appears to be two-dimensional when it is displayed. Yet, this is the case, since computer memory consists simply of an ever-increasing linear list of address spaces.

How pixels are stored in memory

In the next post, I'll discuss about the Applications of Computer Vision. If you have any questions, feel free to leave it in the comment section. Want to get in touch?, You can connect with me on Instagram and LinkedIN Thanks for reading!😄 🙌