"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.
IBM is launching a tool which will analyse how and why algorithms make decisions in real time. The Fairness 360 Kit will also scan for signs of bias and recommend adjustments. There is increasing concern that algorithms used by both tech giants and other firms are not always fair in their decision-making. For example, in the past, image recognition systems have failed to identify non-white faces. However, as they increasingly make automated decisions about a wide variety of issues such as policing, insurance and what information people see online, the implications of their recommendations become broader.
Most people in the deep learning and computer vision communities understand what image classification is: we want our model to tell us what single object or scene is present in the image. Classification is very coarse and high-level. Many are also familiar with object detection, where we try to locate and classify multiple objects within the image, by drawing bounding boxes around them and then classifying what's in the box. Detection is mid-level, where we have some pretty useful and detailed information, but it's still a bit rough since we're only drawing bounding boxes and don't really get an accurate idea of object shape. Semantic Segmentation is the most informative of these three, where we wish to classify each and every pixel in the image, just like you see in the gif above!
Pose estimation, or the ability to detect humans and their poses from image data, is one of the most exciting -- and most difficult -- topics in machine learning and computer vision. Recently, Google shared PoseNet: a state-of-the-art pose estimation model that provides highly accurate pose data from image data (even when those images are blurry, low-resolution, or in black and white). This is the story of the experiment that prompted us to create this pose estimation library for the web in the first place. Months ago, we prototyped a fun experiment called Move Mirror that lets you explore images in your browser, just by moving around. The experiment creates a unique, flipbook-like experience that follows your moves and reflects them with images of all kinds of human movement -- from sports and dance to martial arts, acting, and beyond.
The University at Buffalo announced today that it is launching a multidisciplinary artificial intelligence institute -- the University at Buffalo Artificial Intelligence Institute (UBuffalo.AI). UBuffalo.AI will explore how to combine machines' superior ability to ingest, connect and recall information with concepts that humans excel at, such as reasoning, judgement and strategizing, to develop dynamic human-machine partnerships. To lead UBuffalo.AI, the university recruited David Doermann, PhD, from the University of Maryland (UMD) and the Defense Advanced Research Projects Agency (DARPA). Doermann built his career at UMD developing technologies for document understanding and computer vision for the defense and intelligence communities. Human language is considered one of the grand challenges of AI, and the fundamental and applied research performed in his UMD laboratory has provided a critical foundation for addressing the next wave of AI challenges.
A year ago, when Apple rolled out the iPhone X, one of their most touted features was facial ID. You no longer needed to press a home button or use a passcode. You could unlock your phone with your face. It was the first time I'd really seen facial recognition software being practically used. You probably use something every day with facial recognition software even if you don't realize it--I'm looking at you Snapchat and Instagram face filters.
In 1990, Kai-Fu Lee packed his bags and left Carnegie Mellon University, where he had been teaching artificial intelligence and speech recognition. He headed west to his first Silicon Valley job, running a new group trying to build speech interface technologies at Apple. Eight years later, Lee was hired by Microsoft with a specific mission: to go to China, start a research group, and develop a technology hub--and talent. Today, China's prowess in artificial intelligence can trace many of its roots back to that research group. In a now infamous move, Lee left Microsoft and--after prevailing against the company when it sued him for violating a noncompete agreement--went to Google in 2005 to lead Google China.
In this post we are going to develop a java face recognition application using deeplearning4j. The application is offering a GUI and flexibility to register new faces so feel free to try with your own images. Additionally you can check out the free open source code as part of the PactPub video course Java Machine Learning for Computer Vision together with many new improvements to previous posts applications in java. Face recognition has always been an important problem to solve due its sensitivity in regards to security and because it closely related to people identity. For many years face recognition applications were well known especially in criminology and searching for wanted persons with cameras and sometimes even using satellites.
Art Selfie is powered by computer vision technology based on machine learning. When you take a selfie, your photo is compared with faces in artworks our museum partners have provided. After a short moment, you will see your results along with a percentage to estimate the visual similarity of each match and your face. Your selfie becomes a doorway into art--tap on your lookalike to discover more information about it or an artist that perhaps you've never heard of before. Together with our partner museums we are constantly experimenting with new ways for people to discover and interact with art.
Nothing is as remote as yesterday's utopias. From the 1990s until the end of the last decade, the explosion in computing power was seen by wide-eyed optimists as a force for liberation that would lay low unaccountable authority. Their eyes have narrowed now. Democracy, justice, our very ability to earn a living, feel precarious. "All that is solid melts into air," said Marx of 19th-century capitalism.