Computer Vision Explained

Key Takeaways

hi there and welcome back to 365 data science in today's video we'll explore the AI branch of computer vision we'll cover what computer vision is how computers process images and videos the main families of computer vision models including convolutional neural networks Transformers generative adversarial networks and specialized networks like unet and efficient net then we'll look at real world applications of computer vision let's dive in IBM defines computer vision as an AI field that uses machine learning and neural networks to teach computers to derive meaningful information from digital images and videos consider this analogy if AI functions as the brain computer vision serves as the eyes as humans we effortlessly understand our surrounding environment with all its nuances we can distinguish moving objects changing shapes and different colors sometimes we can notice the slightest differences and the most subtle details the study of computer vision is a Monumental effort to develop sophisticated AI models that allow computers to comprehend real world information to do that computers consume input through images and videos images are simpler because they capture a single moment statically conversely videos are more complex appearing as continuous sequences of images for example 30 frames per second that require processing the computer must analyze each frame and understand its context and continuity next we'll take a look at the different types of computer vision models but before we dive deeper I'd like to take a moment to tell you about 365 data science's learning platform if you're excited about Ai and machine learning we've got the perfect courses to get you started our introduction to AI course provides a comprehensive overview of artificial intelligence while our machine learning in Python course offers hands-on experience with popular ml libraries 365 data science has something for everyone explore our full range of courses and start your AI and machine Learning Journey today now let's get back to our discussion about computer vision there are four main families of computer vision models first we have convolutional neural networks CNN's are foundational for computer vision because they are great when working with high-dimensional data in the early days AI researchers used other types of neural networks for computer vision problems but they struggled with high-dimensional data images due to the immense number of parameters required in contrast cnns are great at capturing spatial hierarchies in images but what does this term mean in plain language it means that CNN's are great at organizing the elements in an image based on their importance and depth imagine looking at a picture where some things seem closer to you and others farther away the foreground is up close the middle ground is between and the background is distant larger objects objects often appear closer and more significant overlapping items make those in front look nearer and centered or top positioned elements typically attract more attention enhancing their importance CNN's are effective at processing information due to their layered structure this enables the network to learn basic features like object edges in initial layers and progressively capture complex highlevel features such as shapes and objects in deeper layers next we'll focus on Transformers a key Topic in our upcoming discussions on generative AI for now we can just say that in some cases AI researchers apply Transformer architecture to images for the purposes of computer vision third generative adversarial networks G's primarily create create lifelike images lastly specialized networks like unet excel in medical image segmentation while efficient net optimizes performance by efficiently scaling neural network dimensions and utilizing computational resources computer vision has a ton of exciting real world applications self-driving cars Medical Imaging security surveillance and other areas we previous discussed show great promise in robotics it's important to remember that computer vision does not need to be part of Robotics to be an incredibly useful product for example consider face recognition software it's just a model built into a software product without a robotic body some of the most exciting advancements in computer vision are currently in virtual reality revolutionizing such Industries as education entertainment and longdistance communication computer vision ai's eyes is a rapidly evolving field that continues to push the boundaries of what's possible in Ai and machine learning now let's recap the key points we've covered in this video computer vision is a field of AI that enables computers to derive meaningful information from images and videos it processes input through images static moments and videos rapid sequences of images the main families of computer vision Vision models include CNN Transformers G and specialized networks like unet and efficient net computer vision has numerous realworld applications from self-driving cars to Medical Imaging and virtual reality thank you for watching this video if you have found this content helpful please like subscribe and follow for more on data science Ai and machine learning until next time keep learning