"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.
Microsoft president Brad Smith speaks at the 2017 annual Microsoft shareholders meeting in Bellevue, WA. (AP Photo/Elaine Thompson) This morning Microsoft President Brad Smith posted an essay on the company's blog that raises important questions about the human rights challenges related to facial recognition technology. Microsoft, and in particular, Smith, have led the tech industry in addressing human rights issues that inevitably grow from the spreading use of emerging technologies. As Smith points out, these new technological capacities are often a force for good, but are also subject to manipulation and can cause great harm. What is clear is that these new technologies are now part of our lives and will play an ever-greater role in the future. Smith rightly focuses on vexing challenges relating to the governance of facial recognition technologies, a rapidly evolving area which requires new models in which both governments and companies assume greater responsibilities.
Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let's see how this thing actually works out in practice in the case of image classification. Before that let's introduce these two terms Keras and Tensorflow and help you build a powerful image classifier within 10 min! Tensorflow is the most used library to develop models in deep learning. It has been the best ever library which has been completely opted by many geeks in their daily experiments .
Let's be real: you are most certainly never going to be as good as Steve Nash, Chris Paul, James Harden -- or really any professional NBA player. But it probably won't stop you from trying to practice or model your game around your favorite players, and spend hours upon hours figuring out how to get better. And while there are going to be plenty of attempts to smash image recognition and AI into that problem, a company called NEX Team is hoping to soften the blow a bit by helping casual players figure out their game, rather than trying to be as good as a professional NBA player. Using phone cameras and image recognition on the back end, its primary app HomeCourt will measure a variety of variables like shot trajectory, jump height, and body position, and help understand how to improve a player's shooting form. It's not designed to help that player shoot like Ray Allen, but at least start hitting those mid-range jumpers.
Machine learning is the idea that describes computers that can essentially "learn" and process new information without specifically being programed to do so. If you give a computer a task, it will more-or-less get better at that task the more it has a chance to engage in it. Object detection is a subset of this idea and is of particular relevance to photos. Not only does object detection let you know which objects are in a photo (hence the name), it also gives you insight into precisely where they are, too. But out of all the industries and activities where object detection is poised to make a big impact, drone services are undoubtedly right at the top.
In this episode of the AI show, the materials for the LearnAI-Bootcamp for Emerging AI Developers will be shared and explained. After watching this video, you'll be able to take the resources and build a CLI application that takes a local directory of images, stores them in blob storage, analyzes them with the Computer Vision API, and puts all that metadata in CosmosDB. You'll also develop a LUIS model for searching pictures with natural language. In the episode that follows, you'll integrate it all in a bot, and search the metadata using Azure Search.
This is the second story in our continuing series covering the basics of artificial intelligence. While it isn't necessary to read the first article, which covers neural networks, doing so may add to your understanding of the topics covered in this one. Teaching a computer how to'see' is no small feat. You can slap a camera on a PC, but that won't give it sight. In order for a machine to actually view the world like people or animals do, it relies on computer vision and image recognition.
Artificial intelligence systems perform some tasks better than humans. In our hospitals, for example, AI systems are being used in medical imaging to analyse images scans to help radiologists to diagnose tumours that the human eye can miss. AI is embedded into our national priorities for education and for research. Almost by stealth, AI is being used to make decisions for us and about us. What's to stop them making decisions without us?