"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.
Imaging in three dimensions rather than two offers numerous advantages for machines working in the factories of the future by granting them a whole new perspective to view the world. Combined with embedded processing and deep learning, this new perspective could soon allow robots to navigate and work in factories autonomously by enabling them to detect and interact with objects, anticipate human movements and understand given gesture commands. Certain challenges must first be overcome to unlock this promising potential, however, such as ensuring standardisation across large sensing ecosystems and increasing widespread understanding of what 3D vision can do within industry. Three-dimensional imaging can be achieved by a variety of formats, each using different mechanics to capture depth information. Imaging firm Framos was recently announced as a supplier of Intel's RealSense stereovision technology, which uses two cameras and a special purpose ASIC processor to calculate a 3D point cloud from the data of the two perspectives.
Apple has published its latest machine learning journal entry with a new article detailing the challenges of implementing facial detection features while maintaining a high level of privacy. Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently on-device. Apple's iCloud Photo Library is a cloud-based solution for photo and video storage.
Tech companies are eyeing the next frontier: the human face. Should you desire, you can now superimpose any variety of animal snouts onto a video of yourself in real time. If you choose to hemorrhage money on the new iPhone X, you can unlock your smartphone with a glance. At a KFC location in Hangzhou, China, you can even pay for a chicken sandwich by smiling at a camera. And at least one in four police departments in the US have access to facial recognition software to help them identify suspects.
When you upload photos to Facebook, have you noticed that the website already seems to know who's in them? It's remarkable, and you can give the credit to big data. Face recognition software, like fraud detection and ad matching algorithms, draws on deep libraries of content in order to deliver the correct results. And these data collections are hard at work across the web and in many of your favorite apps. It comes as no surprise that developers have been hard at work on face recognition software since it's an integral part of security programs.
When launching SAP Leonardo Machine Learning Foundation, SAP started on a mission to overcome these challenges and help all customers transition to the intelligent enterprise, no matter their level of digital maturity and AI expertise. Now, SAP expands the capabilities of its machine learning platform, including the training of image classification services with training data belonging to the customer, the opportunity for customers to deploy their own models on SAP Leonardo Machine Learning Foundation, and new ready-to-use services. To help customers and partners address a larger number of use cases, SAP has opened the enterprise-class model training capability of SAP Leonardo Machine Learning Foundation. It is now possible for customer to tailor services to their business needs by training services on their unique data. This new functionality is enabled via a secure extraction of data via SAP Cloud Platform and predefined training routines.
There is no such thing as foolproof phone security. Case in point: Security researchers at Bkav have reportedly defeated the iPhone X's Face ID feature using a simply-constructed 3D mask. The average person probably doesn't need to worry about the purported hack, but billionaires, celebrities, and high-profile public figures like presidents may want to rethink their use of Apple's nascent facial recognition technology. Apple is trying to convince people Face ID is more secure than its Touch ID fingerprint sensor, which is still used in the iPhone 8 in addition to earlier models. But stories about weak spots (especially if you've got a twin or you're a kid) keep popping up.
It's one of the most wanted features in the iPhone X, but it seems that Face ID may not be as safe as Apple thinks. Cyber-security researchers claim they have fooled the face recognition technology with a mask that costs just £114 ($150) to make. The findings suggest that face recognition is not yet mature enough to guarantee security for computers and smartphones, according to the researchers. The main frame of the face was created with a 3D printer, and the nose was created by an artist from silicone. The eyes were represented with 2D images, while the'skin was also hand-made to trick Apple's AI', according to the researchers.
I'm trying to implement tensorflow object detection API sample. I' following sentdex videos for getting started. The sample code runs perfectly, it also shows the images which are used for testing the results, but no boundaries around detected objects are shown. Just the plane image is displayed without any errors. I'm using this code: This Github link.
What happens when a tech artist and her gene-scientist husband try to wow the crowd at a "Nerd Nite" event in Kendall Square? They pitch an idea for an app to help fight disease by crowd-sourcing millions of 3-D digital maps of human faces. Facetopo was the brainchild of Boston documentarian and artist Alberta Chu and her husband Murray Robinson, whose brother was diagnosed with a rare disease that, like Down's syndrome, can be detected in the face. In a Q&A with Patch, Chu says some day participants could "maybe trade pictures, or eventually, find a twin." "Every user who wants to participate creates a private account and is able to download the app on either IOS or Android where we provide instructions so that you can create a 3-D face map.
On the first day of school, a child looks into a digital camera linked to the school's computer. Upon a quick scan, the machine reports that the child's facial contours indicate a likelihood toward aggression, and she is tagged for extra supervision. Not far away, another artificial intelligence screening system scans a man's face. It deduces from his brow shape that he is likely to be introverted, and he is rejected for a sales job. Plastic surgeons, meanwhile, find themselves overwhelmed with requests for a "perfect" face that doesn't show any "bad" traits.