"... 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
I'll be posting all code and relevant files soon, this demo is part of a tutorial series I'm doing at my university. I'll probably do a twitch stream and eventually YouTube playlist if people like it. Edit: to answer the question I believe in this particular demo I used KCF to track. For gestures I used a convolutional neural net which is both overkill and not the fastest solution, but part of the tutorial is machine learning.
IBM Palo Alto Scientific Center, 2530 Page Mill Road, Palo Alto, CA 94303 Abstract A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples-one in English and one in Chinese-are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms. We are developing a natural language interface to an expert system for message processing.
Expert system software will be a key element of an operator advisory system, a production forecasting system, and a capacity allocation system. Bell Helicopter Textron (Fort Worth, Tex.), a manufacturer of helicopters, has implemented an intelligent system to automate the procurement process. With the new system, the time required for a buyer to purchase a part has been reduced from 1 hour to approximately 10 minutes, increasing productivity by 83 percent. The U.S. Air Force Research Laboratory's Technical Library at the Phillips site on Kirtland Air Force Base, New Mexico, is using advanced pattern-recognition technology to design its virtual library information system. Knowledge-retrieval techniques will be utilized by researchers to access a myriad of information that resides in repositories throughout the state and government.
Eastman Kodak (Rochester, N.Y.), a manufacturer of imaging-related products, has developed an online neural network-based machine vision system for surface mount solder paste inspection. Caere (Los Gatos, Calif.), a provider of neural network-based optical character recognition (OCR) technology, has signed an agreement to supply IBM Ireland with OCR Readers for AN POST, Ireland's national postal service. Using a handheld wand, postal employees will be able to scan text and read bar codes from anywhere on a document. BrainTech (Scottsdale, Ariz.), a developer of neural network and fuzzy logic-based pattern recognition technologies, has signed a development agreement with Raven (Alexandria, Va.), a developer of acoustic systems for the U.S. Navy. BrainTech will integrate its pattern-matching recognition engine into Raven's new medical diagnostic systems.
"If the American public seems a bit confused about the raging debate of security versus civil liberties--Bush/Cheney versus the A.C.L.U.--it may be because the debate itself has been outpaced by technology. In our post-9/11, protowireless world, democracies and free markets are increasingly saturated with prying eyes from governments, corporations and neighbors. For better and worse, free societies are fast entering the world of total surveillance.... Allowing a computer to read your email may not sound threatening, but with advanced pattern-recognition software, scanning many messages over time could produce a powerful consumer profile. As these machines get smarter and smarter, it may soon be far more worrisome to let a machine'read' your information than to have a human reading it.... The hallmarks of the new digital tool-building age are machines that are increasingly smart, small, cheap and communicative.
When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress. In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.
As we all know that machine learning or artificial intelligence has increasingly gained more popularity in the past couple of years and still continues to do so. As at the very moment Big Data is the present trend in the tech industry, machine learning proves to be incredibly powerful when it comes to making predictions or calculated suggestions that are based on large amounts of data. The importance it carries along with the world of amazement it carries is well known and understood. So if an individual wants to learn more about machine learning, how do you start and from where? In order to know about AI/Machine Learning, one not only needs keen interest but also the right resources that can provide the same.
Facebook has launched new tools powered by its facial recognition tech -- the same one that suggests friends to tag in photos. To start with, it has beefed up the alternative text feature it rolled out last year, which describes a photo's contents for people using a screen reader. For instance, the original version of the tool would describe a friend's photo with the words "may contain: tree, sky, sea." The enhanced version will include those and the names of people who could be in the photo even if they aren't tagged. Facebook's facial recognition can be pretty hit and miss, but the names can give visually impaired users a fuller view of the picture.
Here is a quick example for how to get started with some of the more sophisticated point pattern analysis tools that have been developed for ecologists – principally the adehabitathr package – but that are very useful for human data. Ecologists deploy point pattern analysis to establish the "home range" of a particular animal based on the know locations it has been sighted (either directly or remotely via camera traps). Essentially it is where the animal spends most of its time. In the case of human datasets the analogy can be extended to identify areas where most crimes are committed – hotspots – or to identify the activity spaces of individuals or the catchment areas of services such as schools and hospitals. This tutorial offers a rough analysis of crime data in London so the maps should not be taken as definitive – I've just used them as a starting point here.