"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
Today Flying Cloud Technology announces it has entered into an OEM relationship with Wireless Guardian. Wireless Guardian is the world's first forward-facing human threat detection system and the most effective investigative security solution for today's high-tech environment. Providing protection to patrons and facilities, Wireless Guardian tracks both security and pandemic threats up to a mile outside the facility's perimeter. "Flying Cloud is extremely happy to enter into this strategic partnership with Wireless Guardian. We feel that this partnership will showcase the incredible strengths of both companies. Wireless Guardian will be an invaluable data source that is fed into and analyzed by Flying Cloud. This data will allow our joint customers to not only detect someone entering their facility with a temperature, but with our patented AI models, we can clearly show where they went in a facility and show who they were in contact with. Flying cloud is now the only company that can track both the user and the data that they interact with," said Brian Christian, CEO of Flying Cloud Technology.
From left to right, Zongfu Yu, Ang Chen and Efram Khoram developed the concept for a "smart" piece of glass that recognizes images without any external power or circuits. The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds -- and looks -- surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin–Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu.
Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that we're using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. The unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that we're enabling machines to do the thing that our brains seem to do so easily: identify what we're perceiving in the real world around us. The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest.
One of the most widely adopted of the seven patterns of AI is the Patterns and Anomalies pattern. Machine learning is particularly good at digesting large amounts of data very quickly and identifying patterns or finding anomalies or outliers in that data. The "pattern-matching pattern" is one of those applications of AI that itself seems to repeat often, and for good reason as it has broad applicability. The goal of the Patterns and Anomalies pattern of AI is to use machine learning and other cognitive approaches to learn patterns in the data and discover higher order connections between that data. The objective is to determine whether a given data point fits an existing pattern or if it is an outlier or anomaly, and as a result find what fits with existing data and what doesn't.
We provide end-to-end image analysis and vision AI expertise on different business verticals. Your simple data can be turned into a working AI. We will clean and analyze the data you'll provide. We will provide and maintain an AI-powered interface you can use. From trademark searches based on your logo (rather than the traditional text-based searches others offer), AI-powered image search solution for your enterprise, to our generative image-building solution that takes the images you input and develops new iterations.
It belongs to every aspect of our daily lives. Starting from the design and colour of our clothes to using intelligent voice assistants, everything involves some kind of pattern. When we say that everything consists of a pattern or everything has a pattern, the common question that comes up to our minds is, what is a pattern? How can we say that it constitutes almost everything and anything surrounding us? How can it be implemented in the technologies that we use every day?
Face recognition tasks are not handled with regular deep learning approaches. This might be confusing for beginners. We will mention the common stages of a modern face recognition pipeline in this post. We will use DeepFace framework for python in this post. You can install the package with the following command if you haven't install it yet.
Modern face recognition pipelines consist of 4 common stages. These are detection, alignment, representation and verification. These might be confusing for beginners. In this post, we take a step back and mention a face recognition pipeline conceptually. You should follow the links to dive these concepts deep.
Image features For this task, first of all, we need to understand what is an Image Feature and how we can use it. Image feature is a simple image pattern, based on which we can describe what we see on the image. For example cat eye will be a feature on a image of a cat. The main role of features in computer vision(and not only) is to transform visual information into the vector space. Ok, but how to get this features from the image?
Computer vision is a field in computer science that falls under the umbrella of artificial intelligence (AI). Computer vision (CV) software developers strive to give computers the ability to process images in much the same way that humans do. They expect the computer will be able to identify objects, to make appropriate decisions based on what it "sees," and then to produce relevant outputs. Today, facial recognition software, autonomous vehicles, certain forms of surveillance, and gesture recognition are just a few examples of CV systems at work. Why is computer vision so complicated? Every parent can recall their child going through phases when "what's that?" became a recurring question.