The International Association for Pattern Recognition (IAPR) is an international association of non-profit, scientific or professional organizations (being national, multi-national, or international in scope) concerned with pattern recognition, computer vision, and image processing in a broad sense. Normally, only one organization is admitted from any one country, and individuals interested in taking part in IAPR's activities may do so by joining their national organization.

While most would point to home security cameras as the primary application for imaging in the smart home - just this week, after all, smart home darling Nest launched their own home security cam - there appears to be a new focus in the connected home when it comes to imaging tech: our food. Just consider: Last month it was revealed by Science that had been doing research into machine learning around food identification, and had released a new app called Im2Calories, which examines an image and attempts to quantify the amount of calories on a plate. It uses "deep learning" technology - essentially a form of machine learning. Im2Calories can draw connections between what a given piece of food looks like, and vast amounts of available caloric data." And while we're used to Google doing crazy bleeding edge stuff, they're definitely not the only ones who see cameras as a natural fit in the kitchen. Last week we learned of a new product called the June Intelligent Oven, which uses images captured from an in-oven camera to identify food and then automatically program cooking time and temperature. And then there's the SmartPlate, a new product currently on Kickstarter from Fitly that includes three cameras in the plate itself. The cameras are used to detect food quantity and type the image across a database of food and associated caloric content. Wait, a plate with cameras? How exactly does that work? CEO Anthony Ortiz told me that the cameras will be recessed within the plate on the rim. "Think about the cameras having lenses .

A new SwiftKey keyboard hopes to serve you better typing suggestions by utilizing a miniaturized neural network. SwiftKey Neural does away with the company's tried-and-tested prediction engine in favor of a method that mimics the way the brain processes information. It's a model that's typically deployed on a grand scale for things like spam and phishing prevention in Gmail or image recognition, but very recent advancements have seen neural networks creep into phones through Google Translate, which uses one for offline text recognition. According to SwiftKey, this is the first time it's been used on a phone keyboard. To grasp how the new system works, we need to understand the old one.

Pattern Recognition is the official journal of the Pattern Recognition Society. The Society was formed to fill a need for information exchange among research workers in the pattern recognition field. Up to now, we "pattern-recognitionophiles" have been tagging along in computer science, information theory, optical processing techniques, and other miscellaneous fields. Because this work in pattern recognition presently appears in widely spread articles and as isolated lectures in conferences in many diverse areas, the purpose of the journal Pattern Recognition is to give all of us an opportunity to get together in one place to publish our work. The journal will thereby expedite communication among research scientists interested in pattern recognition.

As humans, we can distinguish between different objects easily - such as dogs wearing hats, or between oranges and bananas in a bag - but for computers this has been typically much more difficult. A team of Google researchers has developed an advanced image classification and detection algorithm called GoogLeNet, which is twice as effective than previous programs. It is so accurate it can locate and distinguish between a range of object sizes within a single image, and it can also determine an object within, or on top of, an object, within the photo. A team of California-based Google researchers developed GoogLeNet, that uses an advanced classification and detection algorithm to identify object. The software recently placed first in the ImageNet large-scale visual recognition challenge (ILSVRC).

For years scientists have worked to find a way to make it easier for deaf and hearing impaired people to communicate. And now it is hoped that a new intelligent system could be about to transform their lives. Researchers have used image recognition to translate sign language into'readable language' and while it is early days, the tool could one day be used on smartphones. Researchers have used image recognition to translate sign language (pictured) into'readable language' and while it is early days, the tool could one day be used on smartphones Scientists from Malaysia and New Zealand came up with the Automatic Sign Language Translator (ASLT), which can capture, interpret and translate sign language. It has been tested on gestures and signs representing both isolated words and continuous sentences in Malaysian sign language, with what they claim is a high degree of recognition accuracy and speed.

For some birds, recognising their own eggs can be a matter of life or death. Scientists have used imafe recognition technology to show that birds defending their nests against the Common Cuckoo - which lays its lethal offspring in other birds' nests - have evolved distinctive patterns on their eggs in order to distinguish them from those laid by a cuckoo cheat. These patterns provide a defence against the cuckoo's trickery by helping birds reject the cuckoo eggs before they hatch and destroy the host's own brood. Scientists have shown that birds defending their nests against the Common Cuckoo - which lays its lethal offspring in other birds' nests - have evolved distinctive patterns on their eggs in order to distinguish them from those laid by a cuckoo cheat. Scientists from the University of Cambridge and Harvard University, in Cambridge, Massachusetts, have developed a new computer vision tool to unravel how a host bird may perceive and recognise such complex pattern information.

Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor's degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics. Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service.

Google's Hartmut Neven demonstrates his visual-search app by snapping a picture of a Salvador Dali clock in his office building. Google and other tech companies are racing to improve image-recognition software Computers can recognize some objects in images, but not all Google's engineering director predicts the technology will fully mature in 10 years Google's engineering director predicts the technology will fully mature in 10 years Santa Monica, California (CNN) -- Computers used to be blind, and now they can see. Thanks to increasingly sophisticated algorithms, computers today can recognize and identify the Eiffel Tower, the Mona Lisa or a can of Budweiser. Still, despite huge technological strides in the last decade or so, visual search has plenty more hurdles to clear. At this point, it would be quicker to describe the types of things an image-search engine can interpret instead of what it can't.

Scientists at Google have created artificial intelligence software that can describe the contents of photographs far more accurately than ever before. The software's description of pictures was similar to that written by a human. As well as making it easier to search for images, the software could be used to help blind people understand pictures better, Google said. Stanford University has also announced a breakthrough in the same field. The machine-learning software developed by Google used two neural networks - one which deals with image recognition, the other with natural language processing.