"Image understanding (IU) is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent."
– Image Understanding, by J.K. Tsotos. In Encyclopedia of Artificial Intelligence. Stuart C. Shapiro, editor. 1987. New York: John Wiley & Sons.
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classific... more
Familiarity alters face recognition: Familiar faces are recognized more accurately than unfamiliar ones and under difficult viewing conditions when unfamiliar face recognition fails. Using whole-brain functional magnetic resonance imaging, we found that personally familiar faces engage the macaque face-processing network more than unfamiliar faces. Familiar faces also recruited two hitherto unknown face areas at anatomically conserved locations within the perirhinal cortex and the temporal pole. These two areas, but not the core face-processing network, responded to familiar faces emerging from a blur with a characteristic nonlinear surge, akin to the abruptness of familiar face recognition.
The technology is expected to replace the Apple TouchID fingerprint sensor which has been there in iPhones since iPhone 5S. It seems, just like the ARKit, Apple might provide a BiometricKit to developers for creating more applications for the face recognition sensor inside the device's front camera -- it could be used for more than just unlocking the device. Chances are that the upcoming device features facial recognition, but without a TouchID sensor. Read: iPhone 8's Facial Recognition Feature To Be Used For Apple Pay Payments According to some reports, TouchID could be moved to the edge of the handset alongside the power button, however, this hasn't been done before and the possibility of Apple altering the form factor of its device to accommodate a TouchID sensor doesn't seem very high.
Every time North Korea launches a missile, experts pore over photographs and videos to learn more about the country's weapons capabilities. If you know the video scale and the frame rate, you can determine the position-time data for a moving object. North Korea identified the missile as the Hwasong-14, which is believed to have a length of 18 meters (although it could be 16). If you know the camera's angular field of view, you could estimate the missile's altitude.
'We predict the OLED model won't support fingerprint recognition, reasons being: the full-screen design doesn't work with existing capacitive fingerprint recognition, and the scan-through ability of the under-display fingerprint solution still has technical challenges' He also claims the last minutes change will not mean delays. 'As the new OLED iPhone won't support under-display fingerprint recognition, we now do not expect production ramp-up will be delayed again (we previously projected the ramp-up would be postponed to late October or later).' The HD video claims to show an iPhone 8 dummy unit, providing a 360-degree look at the smartphone. The video was shared with DailyMail.com The video appears to confirm that the iPhone 8 will feature a vertical dual-lens camera that won't sit flush with the device As well as the hands-on look at the device, the video also uses a ruler to show the dimensions of the phone.
A few notable exceptions, like DeepMind's recently released Kinetics dataset, try to alleviate this by focusing on shorter clips, but since they show high-level human activities taken from YouTube videos, they fall short of representing the simplest physical object interactions that will be needed for modeling visual common sense. To generate the complex, labelled videos that neural networks need to learn, we use what we call "crowd acting". Predicting the textual labels from the videos therefore requires strong visual features that are capable of representing a wealth of physical properties of the objects and the world. The videos show human actors performing generic hand gestures in front of a webcam, such as "Swiping Left/Right," "Sliding Two Fingers Up/Down," or "Rolling Hand Forward/Backward."
The nation's top-level intelligence office, the Director of National Intelligence, wants to find "the most accurate unconstrained face recognition algorithm." asks a posting on challenge.gov The goal of the Face Recognition Prize Challenge is to improve core face recognition accuracy and expand the breadth of capture conditions and environments suitable for successful face recognition. The government noted that there has been "enormous research" done in the field, and it wants "to know whether this rich vein of research has produced advancements in face recognition accuracy." The most accurate algorithms submitted to the government for the contest are eligible to split a pot of $50,000, according to the contest rules.
A research team led by Professor Hoi-Jun Yoo of the Department of Electrical Engineering has developed a semiconductor chip, CNNP (CNN Processor), that runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. To accomplish this, the research team proposed two key technologies: an image sensor with "Always-on" face detection and the CNNP face recognition chip. The face detection sensor combines analog and digital processing to reduce power consumption. The second key technology, CNNP, achieved incredibly low power consumption by optimizing a convolutional neural network (CNN) in the areas of circuitry, architecture, and algorithms.
South Wales Police didn't provide details about the nature of the arrest, presumably because it's an ongoing case. Back in April, it emerged that South Wales Police planned to scan the faces "of people at strategic locations in and around the city centre" ahead of the UEFA Champions League final, which was played at the Millennium Stadium in Cardiff on June 3. "It was a local man and unconnected to the Champions League," a South Wales Police spokesperson told Ars. We know from the request for tender published by the South Wales Police, however, that the man's face was probably included in the force's "Niche Record Management system," which contains "500,000 custody images."