"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.
Amazon Rekognition backed by AWS cloud services is another giant in the face recognition space. Amazon Rekognition is the easiest way to add features relating to image or video processing to your application, especially if you are running your application on AWS cloud. The service can identify faces, people and activities among many other things once given image content. Amazon Rekognition is very popular for its facial analysis service. It has a fairly accurate facial recognition engine that works on many kinds of images and videos also.
Intel on Tuesday is expanding its family of RealSense cameras, extending the depth-sensing capabilities of the D400 series. The new D435i camera includes an inertial measurement unit (IMU), providing developers and engineers with another data point to work with as they build drones, robots and other products. RealSense cameras are used to help products "see" the world around them in 3D by tracking movement and depth. In an image from a depth camera, each pixel has four values: red, green blue and depth. The colors align with the depth of an object in an image -- red objects are farther away, while green images are closer and blue images are closest.
A robot that communicates with humans via facial expressions and understands people by scanning their face has been stumped when it met a person with botox. Furhat Robotics unveiled its'world's most advanced social robotics and conversational artificial intelligence platform' last week. The android can communicate with humans in the way we do with each other - by speaking, listening, showing emotions and reading changes to facial features. The Stockholm-based start-up were left scratching their heads when one test subject completely threw the eerily-lifelike robot. A Furhat insider said: 'We were at a loss as to why one of our robots wasn't interacting properly with a human test subject.
Scientists have taught a robot how to use human-like hand gestures while speaking by feeding it footage of people giving presentations. The android learned to use a pointing gesture to portray'you' or'me', as well as a crooked arm action to suggest holding something. Building robots that gesticulate like humans will make interactions with them feel more natural, the Korean team behind the technology said. They built the robot around machine learning software that they showed 52 hours of TED talks - presentations given by expert speakers on various topics. Pictured is a Pepper robot that scientists taught to give human-like hand gestures during speech.
Deep learning is a technology with a lot of promise: helping computers "see" the world, understand speech, and make sense of language. But away from the headlines about computers challenging humans at everything from spotting faces in a crowd to transcribing speech -- real-world performance has been more mixed. One deep-learning technology whose real-world results have often disappointed has been facial-recognition. In the UK, police in Cardiff and London used facial-recognition systems on multiple occasions in 2017 to flag persons of interest captured on video at major events. Unfortunately, more than 90% of people picked out by these systems were false matches.
The algorithm, which performs more robustly than leading methods, aims to solve a disconnect in computer vision and robotics, namely, that most robots currently do not have the perception they need to be able to handle disturbances in the environment. This work is important because it is the first time in computer vision that an algorithm trained only on synthetic data (generated by a computer) is able to beat a state-of-the-art network trained on real images for object pose estimation on several objects of a standard benchmark. Synthetic data has the advantage over real data in that it is possible to generate an almost unlimited amount of labeled training data for deep neural networks.
Toronto's Humber River Hospital (HRH), which opened in 2015, is the first hospital in the world to install a medical imaging "tile"--or app--into its NASA-style, 4,500 square-foot digital command center in collaboration with GE Healthcare Partners. Recognized as North America's first fully digital hospital, installing a medical imaging tile into its digital command center in July 2018 made sense for HRH, which serves a region of more than 850,000 people. Six other hospitals in the U.S. and one in Europe have already installed or have plans to open command centers next year, a GE spokeswoman told HealthImaging. By 2020, GE hopes digital command centers will become a feature hospitals can't survive without. Essentially, HRH's command center is a literal wall continuously processing real-time data from multiple source systems across the hospital.
Amazon employees plan to take CEO Jeff Bezos to task about the firm's controversial facial recognition software, Rekognition. The tech giant will host an all-staff meeting on Thursday and it's there that employees will flood executives with questions about Rekognition, as well as why Amazon continues work with immigration authorities, according to Recode. Pressure has been mounting for Amazon to cancel its contracts with ICE and law enforcement agents, which allow them to test out the facial recognition technology. Amazon employees plan to take CEO Jeff Bezos (pictured) to task at an all-hands meeting on Thursday about the firm's controversial facial recognition software, Rekognition Amazon lets employees submit their questions for Bezos and other executives beforehand using an online form. They then go through the list and decide on which questions to answer.
Why do some mobile in-app ad campaigns succeed, while others fall flat? In part, it's because the creatives used are just ineffective. Too often, ads go unseen -- and unclicked. In the second quarter of 2018, Moat's average valid and viewable rate was around 60 percent, while the average viewable rate noted by IAS in the same time frame was less than 50 percent. That means two of every five ads will never be seen in full by a real person.