Image Processing

Sometimes recognition software is excellent at correctly categorizing certain types of images but totally fails with others. Some image recognition engines prefer cats over dogs, and some are far more descriptive with their color knowledge. But which is the best overall? Perficient Digital's image recognition accuracy study looked at image recognition -- one of the hottest areas of machine learning. It looked at Amazon AWS Rekognition, Google Vision, IBM Watson, and Microsoft Azure Computer Vision to compare images.

A lot of startups in the San Francisco Bay Area claim that they're planning to transform the world. San-Francisco-based, Elon Musk-founded OpenAI has a stronger claim than most: It wants to build artificial general intelligence (AGI), an AI system that has, like humans, the capacity to reason across different domains and apply its skills to unfamiliar problems. Today, it announced a billion dollar partnership with Microsoft to fund its work -- the latest sign that AGI research is leaving the domain of science fiction and entering the realm of serious research. "We believe that the creation of beneficial AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity," Greg Brockman, chief technology officer of OpenAI, said in a press release today. Existing AI systems beat humans at lots of narrow tasks -- chess, Go, Starcraft, image generation -- and they're catching up to humans at others, like translation and news reporting.

Microsoft is investing $1 billion in OpenAI to support us building artificial general intelligence (AGI) with widely distributed economic benefits. We're partnering to develop a hardware and software platform within Microsoft Azure which will scale to AGI. We'll jointly develop new Azure AI supercomputing technologies, and Microsoft will become our exclusive cloud provider--so we'll be working hard together to further extend Microsoft Azure's capabilities in large-scale AI systems. Each year since 2012, the world has seen a new step function advance in AI capabilities. Though these advances are across very different fields like vision (2012), simple video games (2013), machine translation (2014), complex board games (2015), speech synthesis (2016), image generation (2017), robotic control (2018), and writing text (2019), they are all powered by the same approach: innovative applications of deep neural networks coupled with increasing computational power.

Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor in the Department of Radiology and BRIC at UNC-Chapel Hill. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1,000 papers in the international journals and conference proceedings. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors of MICCAI Society, in 2012-2015, and will be General Chair for MICCAI 2019.

We invite you to join Intel's Next Generation & Standards (NGS) Group and the 5G revolution! We are a global team of passionate engineers and technologists from diverse industry backgrounds, working together to realize a world of connected computing. Intel's NGS team is chartered with developing advanced prototypes of various technologies to deliver innovative and state of the art wireless experiences into the market within the Internet of Things, 5G Next Generation Wearable and IoT solutions and other world-class wireless connectivity technologies and products In this position, you will be working on advanced algorithm design, development and innovation towards the future emerging technologies in the Next Generation and Standards (NGS) group within Intel. PhD or Master's degree in Electrical Engineering, Computer Science, Video/Graphics/Image Processing, Computer Vision, Signal Processing or other related areas: The Next Generation and Standards Group's mission is to lead standards, ecosystem, and prototyping development efforts across Intel for advanced wireless communications IP, starting with 5G and beyond. This group brings together a panorama of competencies including standards creation, ecosystem development, use case and business development in creating advanced prototypes and technologies that propel Intel's leadership.

Artificial Intelligence (AI) is not considered just an emerging technology with a bright future, it is indeed a robust growing platform, impacting several industries and touching numerous spheres of life. AI algorithms need enormous volumes of datasets to be trained appropriately, after which the system can not only decipher pictures, such as recognizing a dog is a dog or differentiating a chair from a table, it can also generate original images and create exceptionally amazing artistry of quality associated with those of Picasso or Michelangelo. AI model that makes it possible has matured substantially over the recent years and it produces perfect output for certain applications but needs more refinement in other cases. Computer scientists have spent around two decades to teach, train and build machines which can visualize the world around them, a normal skill that humans take for granted, yet it's one that's highly challenging to train a machine to do, kudos to artificial intelligence for making it possible!! Two major ground-breaking improvements in AI image processing have been facial-recognition technology in both retail and security, as well as image generation in all fields of art. The commercialized usage of facial recognition technology is to improve sales and marketing of products including efficient targeting of audience.

The name of the window can be anything, but it should be the same because this acts like the id of the window.

In this video you will learn how to practically apply machine learning. We are showing an example of how to use our image classification service via an easy-to-use web service. More information about SAP Cloud Platform and its services can be found at https://cloudplatform.sap.com.

Eyes are more than the "windows to the soul." As such, ocular health and neurological health are intertwined. The most skilled ophthalmologists can read ocular scans to not only look for eye disease, but also traces of a host of neurological disorders. Voxeleron is using artificial intelligence and machine learning to, as they put it, "democratize expertise." Their algorithms hold the promise of delivering expert-level diagnostic capabilities to any lab with a scanning device.

Since their introduction more than a decade ago, smartphones have been equipped with cameras, allowing users to capture images and video without carrying a separate device. Thanks to the use of computational photographic technologies, which utilize algorithms to adjust photographic parameters in order to optimize them for specific situations, users with little or no photographic training can often achieve excellent results. The boundaries of what constitutes computational photography are not clearly defined, though there is some agreement that the term refers to the use of hardware such as lenses and image sensors to capture image data, and then applying software algorithms to automatically adjust the image parameters to yield an image. Examples of computational photography technology can be found in most recent smartphones and some standalone cameras, including high dynamic range imaging (HDR), auto-focus (AF), image stabilization, shot bracketing, and the ability to deploy various filters, among many other features. These features allow amateur photographers to produce pictures that can, at times, rival photographs taken by professionals using significantly more expensive equipment.