Tag: Vision

How does our brain see Jesus face on a tortilla?
Whether its New Hampshires erstwhile granite Old Man of the Mountain, or a face on Mars, our brains are adept at locating images that look like faces. nonface Sinha and his students set out to investigate how that brain region decides what is and is not a face, particularly in cases where an object greatly resembles a face. To help them do that, the researchers created a continuum of images ranging from those that look nothing like faces to genuine faces. They found images that very closely resemble faces by examining photographs that machine vision systems had falsely tagged as faces. (more)

The disruptive power of gesture and voice recognition
Similarly, Apple's Siri virtual assistant has taught manufacturers and software developers that voice recognition has moved beyond recognition and into comprehension. And that's happening largely as a consequence of the rapid increase in microchip processing power, said Aviad Maizels, founder and president of PrimeSense, which designed the Kinect's chips. They've since crossed that threshold, and continued improvements in processing power are enabling more sophisticated gesture recognition tools. The increase in processing power has also helped improve speech-recognition software, said Richard H. The next wave, represented by Nuance's Dragon TV and the forthcoming Vlingo TV app, will help people search through program guides, answer questions about shows and exchange messages with friends while they watch TV. (more)

This Video Shows How Computers See the World
But a new video called Robot Readable World makes the argument for a slightly more complex form of robot vision. Robots and computers currently process the video that we give them using complicated algorithms, and that software is starting to give computers and robots their own distinct way of seeing. compiles over 30 different examples of video recognition technologies (all listed on the video's description), and puts them over an unnerving soundtrack (a song called Cold Summer Landscape by the band Blear Moon) to create a vision of how computers break complex forms and movement down into something they can understand. Seeing the kind of intense processing that it takes for a robot to do a simple task like turning a corner really drives home how, even as robots gain the ability to replicate more and more human actions, the machines were building are thinking in fundamentally different ways. (more)

Using Color in Machine-Vision Applications
Areas of focus include: Color cameras and how to obtain a color image from an RGB-based camera Bayer interpolation and how it is performed, showing the different methods and results Camera calibration including the effects of different lighting conditions on color images The primary color models used in machine vision: RGB, HSI, CIE 1931 XYZ color space, and its derivatives such as CIELUV, CIEUVW, and CIELAB Which color models are best for which types of application Established in 1976, Matrox Imaging is a leading developer of component-level solutions for machine vision, image analysis, medical imaging, and video surveillance. This drive for innovation has led to many industry firsts, including the worlds first and smallest 1394b digital camera, the smallest GigE camera and the first and first USB 3.0 machine vision camera to market....... (more)

Neural network learns to identify group sizes without knowledge of numbers
After feeding the network 51,800 images, where each was a unique layout of rectangles of various sizes, the researchers found that the new images generated by the system began to demonstrate an awareness of the relative size of different groups without having to perform any counting. Next, to demonstrate, ANS, the team fed the system another program that allowed the system to compare different groups that it had seen during the first run and found, based on new images generated, that the system was able to make educated guesses about which was bigger or smaller; Teaching computer systems to learn to use ANS is but one step towards creating machines that think rather than simply crunch numbers for us, and the hope is that one day, such systems can be put into robots to make them as useful as those weve seen in movies for decades. (more)

Artificial intelligence: Getting better at the age guessing game
The active learning algorithm is faster and more accurate in guessing the age of an individual than conventional algorithms. They have, for example, developed computer algorithms for facial age classification -- the automated assignment of individuals to predefined age groups based on their facial features as seen on video captures or still images. A person can teach a computer to make better guesses by running its algorithm through a large database of facial images of which the age is known using sets of labeled images, but acquiring such a database can be both time-consuming and expensive. The technology could find use, for example, in digital signage where the machine determines the age group of the viewer and displays targeted advertisements designed for those age groups, or in interactive games where the machine automatically presents different games based on the players' age range. (more)

AI to predict sun's next attack on Earth
JUST before noon on 1 September 1859, an English solar astronomer named Richard Carrington witnessed the biggest solar flare ever recorded. So researchers are now turning to automated image-processing and artificial intelligence to better forecast the sun's behaviour and give us time to prepare for a solar onslaught. Over the past two decades, several solar flares and magnetic storms of varying intensity have hit Earth. Solar observatories that study the sun continuously should be able to give us some warning before an impending storm. (more)

Google+ Photos Get Automatic 'Find My Face' Recognition
Google+ Photos Get Automatic 'Find My Face' Recognition Google on Thursday announced that it will make facial-recognition technology available for photos uploaded to its Google+ social network. With Find My Face, "Google+ can prompt people you know to tag your face when it appears in photos," Google's Matt Steiner wrote in a Google+ post. When a Google+ user uploads a photo, for example, they will receive a prompt to opt-in to Find My Face, turn the feature on (see image below), or say no, Petrosky said. If you upload dozens of photos from a party the night before, the facial-recognition technology will look through those photos and suggest people to tag; Like Find My Face, the Picasa tool prompts users to identify the people in uploaded photos, after which it starts suggesting tags for photos based on facial similarities. (more)

Computer vision: Cheat Sheet
Computer vision? You're on the right track with your first guess - computer vision has nothing to do with 'computer vision syndrome' so hold off on buying a new pair of glasses. Such visual-motor processing tasks are trivial for the average sighted human but, looked at from a computer's point of view, they are highly complex problems, requiring sophisticated image processing software that analyses the data to recognise key objects and features. In short, the problem tackled by computer vision research is translating the flat 2D data a computer 'sees' into a 3D real-world reality that it can recognise and understand. (more)

Developing artificial intelligence systems that can interpret images
Today, Torralba is a tenured associate professor of electrical engineering and computer science at MIT, and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he develops AI systems that can interpret images to understand what scenes and objects they contain. I wanted to build systems that could put objects into context, to try to understand how different objects relate to each other, he says. So he began developing systems that used information gathered from the entire image to help identify individual objects. If an image contains an object perched on top of a table, for example, that object is unlikely to be anything very large, narrowing down considerably the number of things it could possibly be. (more)

Scientists Model Brain to Teach Computers to Recognize
Researchers at the beginning of the computer revolution assumed that teaching a computer to recognize something would be easy. Researchers from Los Alamos National Laboratory, Chatham University, and Emory University created a neural network with a slightly different structure than what is usually used in research. Most neural network research involves wiring artificial neurons from one layer to others in another layer. In this case, the researchers decided to wire some neurons to other neurons in the same layer, creating lateral connections. (more)

Have We Met? Tracing Face Blindness to Its Roots
Those with prosopagnosia, also known as face blindness, can see perfectly well, but their brains are unable to piece together the information needed to understand that a collection of features represents an individuals face. One of the keys to understanding face recognition, it seems, is understanding how the brain comes to recognize voices. But by testing for these two conditions simultaneously, researchers at the Max Planck Institute for Human Cognitive and Brain Sciences in Germany recently found evidence that face and voice recognition may be linked in a novel person-recognition system. The researchers found that regions of the brain already associated with facial recognition, like the fusiform face area in the occipital lobe, are directly linked to regions responsible for voice recognition, mostly in the temporal lobe. (more)

Do-It-Yourself Cat Door Recognizes Your Feline
The ideal cat door would let the cat in most of the time, but lock the door when kitty is carrying prey or when another animal, such as a raccoon, approaches the door.Wikimedia Commons What does it take for a computer program to recognize your cat? At the time, the door connected to a desktop computer that ran the program that snapped pictures of Flo and analyzed them as she approached the door. Now, Forster is determined to build Flo's door for Timothy on weekends, in between his usual consulting work in Simi Valley, Calif. But because computer vision is still difficult, even for scientists, people who are looking for guaranteed results, rather than the fun of putting the door together, should probably stay away from a do-it-yourself door for now. (more)

Health Discovery Corporation's Popular MelApp Featured in Men's Health Magazine
First launched last summer for iPhone, MelApp uses highly sophisticated patent protected mathematical algorithms and image based pattern recognition technology to analyze an uploaded image. In addition, MelApp can use the smartphones GPS to refer users to physicians specializing in the diagnosis and treatment of melanoma for proper medical diagnosis and treatment. We are very proud of the fact that we continue to hear from people who have used MelApp and as a result they have made appointments with their physicians to have a proper medical examination and biopsy which led to a diagnosis of early melanoma still in the curable stage. About Health Discovery Corporation Health Discovery Corporation is a molecular diagnostics company that uses advanced mathematical techniques to analyze large amounts of data to uncover patterns that might otherwise be undetectable. (more)

Building machines that see: Finding edges in images
Eric Gregori, BDTI 5/9/2012 4:11 PM EDT With the emergence of increasingly capable low-cost processors and image sensors, its becoming practical to incorporate computer vision capabilities into a wide range of embedded systems, enabling them to analyze their environments via image and video inputs. Then, after explaining edge detection from an algorithmic perspective, we show how to use OpenCV, a free open-source computer vision software component library, to quickly implement application software incorporating edge detection. If you graph the values of a single row of pixels containing an edge, the strength of the edge is shown by slope of the line in the graph. Grayscale soft edge Figure 2 above and Figure 3 below represent eight pixels from two different parts of a grayscale image. (more)

Assistive Technology Computer Products for Low Vision Designed by Ai Squared ...
The products from Ai Squared are available in 20 languages and are sold in 45 countries throughout the world. Their state-of-the-art products include ZoomText, ZoomText USB, the ZoomText Large-Print Keyboard and ZoomText Express. They introduced their flagship product, ZoomText, in 1988 and as the popularity of this product grew, the company shifted its focus to the exclusive development and marketing of software for the visually impaired. Their state-of-the-art products include ZoomText, ZoomText USB, the ZoomText Large-Print Keyboard and ZoomText Express. (more)

Talk on supercomputers and machine vision
St Martins Institute of IT is organising a talk by two prominent academic researchers and a number of readers in the field of supercomputers and brain-inspired machine vision. His current research is in image processing, computer vision and pattern recognition, and includes computer simulations of the visual system of the brain, computer applications in health care and life sciences and creating computer programs for artistic expression. thesis in theoretical physics at Wuppertal University on simulations of lattice quantum chromodynamics, and at Groningen University in the field of parallel computing with systolic algorithms. His research interests include lattice gauge theories, quantum computing, numerical and parallel algorithms, and cluster computing. (more)

Potato industry reaps benefits of computer vision
Social Media Tools A prototype computer vision system that can identify sub-standard potatoes has been developed by computer scientists from the University of Lincoln (Lincoln, UK). Now, working with the UK Potato Council, the University of Lincoln team from the Centre for Vision and Robotics Research has produced a low-cost system that uses off-the-shelf hardware and bespoke software to detect, identify and quantify common defects affecting potatoes. Director of the Centre for Vision and Robotics Research Dr. Tom Duckett says, The system relies on initial input by an expert, identifying blemishes, diseases, as well as good specimens, from sample batches of potatoes. However, for white potato varieties, greening tends to look green, whereas for red potato varieties, greening tends to look more black than green. (more)

IU Bloomington undergraduates honored for outstanding research, creative activity
-- Six undergraduate students at Indiana University Bloomington have been recognized for projects that include developing a computer vision program for identifying birds; Created in 2010, the Provost's Award for Undergraduate Research and Creative Activity is sponsored by the offices of the provost, the vice provost for undergraduate education, and the vice provost for faculty and academic affairs. But, working with Crandall and IU biologists, he was able eventually to develop an algorithm that could consistently identify bird species. Karissa McKelvey, a senior in the School of Informatics and Computing from Santa Rosa, Calif., worked under the supervision of professor Filippo Menczer on the Truthy project, which analyzes and makes accessible the massive stream of data disseminated through social media. She recently presented her work in a peer-reviewed paper for the 2012 Conference on Computer Supported Cooperative Work in Seattle...... (more)

David L. Waltz, Computer Science Pioneer, Dies at 68
During his career as a teacher and a technologist at start-up companies as well as large corporate laboratories, Dr. Waltz made fundamental contributions to computer science in areas ranging from computer vision to machine learning. Dr. Waltz graduated in 1972, then taught computer science at the University of Illinois at Urbana-Champaign and, later, at Brandeis University in Massachusetts. Thinking Machines was an early maker of massive, parallel supercomputers, and by joining the company, in 1984, Dr. Waltz gained access to computers that by 80s standards held vast amounts of fast random-access memory, up to 512 megabytes. For the first time it was possible to use simple algorithms with lots and lots of data, said Brewster Kahle, a computer scientist who directs the Internet Archives and was one of the Thinking Machines researchers. (more)

Facial recognition software spots family resemblance
FACIAL recognition software that's as good as people at spotting family resemblances could help to reunite lost family members - or help the likes of Facebook work out which of your friends are blood relatives. To do this, the team used a database of public figures and their parents or children - such as French president Nicolas Sarkozy and his son Jean - and fed the program 320 pairs each of parent-child matches and mismatches. The software then compared the difference between a test pair of photos with pairs of photos in its database. In tests using 160 pairs - 80 parent-child matches and 80 mismatches - the system had a success rate of 68 per cent. (more)

Yes folks, it's artificial artificial artificial intelligence
Yes folks, its artificial artificial artificial intelligence This is about using what The Economist calls artificial artificial intelligence (like Mechanical Turk, which uses people as artificial computers) to enhance (artificially intelligent) machine vision The idea is that the disabled can finally turn the tables on disability. Bigham of Rochester University called: Real-Time Crowd Support for People with Disabilities It was given at Dartmouth College in New Hampshire, co-sponsored by the Computer ScienceColloquiumand the the Institute for Security, Technology, and Society on November the 15th, 2011 Heres an introduction to the talk: The past few decades have seen the development of wonderful new intelligent technology that serves as sensors and agents onto an inaccessible world for people with disabilities, but it remains both too prone to errors and too limited in the scope to reliably address many problems faced by people with disabilities in their everyday lives. (more)

Digital Image Processing and Analysis (2e)
In fact it is a fairly traditional account of mostly linear approaches to image processing. It makes use of CVIPtools - the Computer Vision and Image Processing Algorithm Test and Analysis Tool (CVIP-ATAT) and the CVIP Feature Extraction and Pattern Classification Tool (CVIP-FEPC), which I have to admit I hadn't encountered before reading this book. The book starts off with two chapters covering the obligatory look at computer imaging systems - mostly it is about the CVIPtools but it also covers file formats. Section II is where the real work starts. (more)

Embedded Vision Alliance debuts
Embedded Vision Alliance debuts Founded in May 2011, the Embedded Vision Alliance now has 17 member companies. Founded in May 2011 by BDTI (Berkeley Design Technology Inc.), an independent technology-analysis and engineering-services firm, the Embedded Vision Alliance now has 17 member companies, said Jeff Bier, BDTI president. This will create new markets and high-growth opportunities for suppliers of electronic vision equipment and components, Bier said. Industrial machine-vision suppliers that are currently vertically integrated may want to consider broader-sourced business models and explore opportunities in other vision markets. (more)

How Google's Self-Driving Car Works
Once a secret project, Google's autonomous vehicles are now out in the open, quite literally, with the company test-driving them on public roads and, on one occasion, even inviting people to ride inside one of the robot cars as it raced around a closed course . Thrun and Urmson explained how the car works and showed videos of the road tests, including footage of what the on-board computer "sees" [image below] and how it detects other vehicles, pedestrians, and traffic lights. The car then combines the laser measurements with high-resolution maps of the world, producing different types of data models that allow it to drive itself while avoiding obstacles and respecting traffic laws. The vehicle also carries other sensors, which include: four radars, mounted on the front and rear bumpers, that allow the car to "see" far enough to be able to deal with fast traffic on freeways; The second thing is that, before sending the self-driving car on a road test, Google engineers drive along the route one or more times to gather data about the environment. (more)

Vision-guided robot automates vegetation analysis
After two sets of images are captured by the system under different stages of plant growth and illumination conditions, each image is processed using algorithms provided by MATLAB from The MathWorks (Natick, MA, USA). Each RGB image is captured, then converted to a normalized excessive green (NEG) channel, represented by NEG = 2.8 ( g/r + g + b ) ( r/r + g + b ) ( b/r + g + b ) to emphasize the green channel. Before training, these images were pre-processed to measure specific morphological features of the plants within the images. After the plant perimeter, inner area, width, and height of a plant were measured, the features converted to five normalized featuresheight/width, height/perimeter, perimeter/area, width/area, and height/areato minimize the influences of the image size of each plant. (more)

Pollen research not to be sniffed at
POLLEN may annoy allergy sufferers in springtime but, viewed under the microscope, a pollen grain is a thing of beauty. The exhibition Pollen Under the Microscope celebrates the purchase of cutting-edge microscope technology to identify pollen grains and speed up our understanding of nature. Some of the smallest images come from the new microscope technology, the Pollen Classifynder system, developed by Massey University in New Zealand. CSIRO and the Atlas of Living Australia purchased the microscope and automated image detection system to rapidly identify pollen - the tiny DNA-carrying grains so vital to agriculture and biodiversity. (more)

Software to prevent abuse at the click of a mouse
Teaming up with investigators from the State Office of Criminal Investigation in Berlin, Fraunhofer researchers have come up with an automated assistance system for image and video evaluation that can detect child-pornographic images from among even large volumes of data. Many are avid data collectors: when suspects data media are confiscated, detectives must often click their way through hundreds of thousands of files to find the illegal images they seek. The algorithms use up to several thousand characteristics that describe properties such as color, texture and contours in order to analyze whether an image depicts child abuse. If the system is run on a standard PC, it classifies up to ten images per second, drastically accelerating detectives investigations. (more)

Speech Recognition Leaps Forward
Speech Recognition Leaps Forward During Interspeech 2011 , the 12th annual Conference of the International Speech Communication Association being held in Florence, Italy, from Aug. 28 to 31, researchers from Microsoft Research will present work that dramatically improves the potential of real-time, speaker-independent, automatic speech recognition. Dong Yu , researcher at Microsoft Research Redmond , and Frank Seide , senior researcher and research manager with Microsoft Research Asia , have been spearheading this work, and their teams have collaborated on what has developed into a research breakthrough in the use of artificial neural networks for large-vocabulary speech recognition. The notion of using ANNs to improve speech-recognition performance has been around since the 1980s, and a model known as the ANN-Hidden Markov Model (ANN-HMM) showed promise for large-vocabulary speech recognition. The new project applied CD-DNN-HMM models to speech-to-text transcription and was tested against Switchboard, a highly challenging phone-call transcription benchmark recognized by the speech-recognition research community. (more)

Face recognition technology fails to find UK rioters
THE response was as aggressive and swift as the riots themselves. Within a few hours of the worst of last week's looting across London and other English cities, attempts were being made to use CCTV footage to track down the individuals who had plundered shops and destroyed buildings. But those raised on a diet of TV police dramas who expected crack law enforcement teams to simply plug the footage into a computer and then print out a list of suspects are going to be disappointed. One of the most common methods used to help identify an individual from camera footage is photoanthropometry, which uses "proportionality indices" to compare a picture of a suspect on a police database, say, with a CCTV image. (more)

Pongr Computer Vision Used by Pepsi for Photo-Scanning Contest
Digital Signage Featured Article Pongr Computer Vision Used by Pepsi for Photo-Scanning Contest Pongr engineers excel in image-recognition technology. Nearly 300 million cases of Pepsi product are being branded with THE X FACTOR promotion, an opportunity to win 56 grand prizes of trips to Los Angelesto see the show broadcast live. Pongr technology recognizes company logos without the aid of ugly bar codes. " "By using our packaging as digital media and as a conduit in to the show, we are bringing the physical and digital worlds together for Pepsi consumers and THE X FACTOR fans," said Shiv Singh, PepsiCo Beverages Global Head of Digital. (more)

Clever Cornell Robot Finds your Lost Keyboard
The same research group at Cornell has also been working on clever ways to allow robots to efficiently interpret scenes and identify objects, which is one of those things that robots are going to have to be good at before they can really become helpful in our homes. Humans have the ability to look at a scene and immediately pick out important elements while ignoring everything else, because we have brains that are awesome like that. So if you ask a robot go to find you (say) a computer keyboard, it's got to enter a room and methodically search every pixel-equivalent area until it finds what it's looking for. What the Cornell research group has been teaching robots to do is to be able to rapidly break down a scene into general categories, and then recognize how some categories are related to others. (more)

Diffbot Sees The Web Like People Do, Now Free For Developers
Today, Diffbot is releasing its first set of APIs, now open to all developers for free. The launch has the potential to dramatically impact the types of applications developers can build, and for consumers, it means a whole host of intelligent applications are about to emerge. The New APIs: On-Demand & Follow With the two APIs available now, developers can build apps that automatically extract meaning from pages, apps that understand whats trending and whos talking about it, apps that provide RSS feeds where none were available before and apps that read just the relevant parts of webpages aloud, ignoring ads, header and footer copy. Or FeedBeater , which makes it easy to turn any URL into an RSS feed automatically ( one of Diffbots first creations ). (more)

New App Can ID Complete Stranger's Facebook and Social Security No.
Please input the letters/numbers that appear in the image below. PittPatt jumps online and compares that picture to millions of images in Facebook and in Google Inc.'s ( GOOG ) image search, using advanced facial recognition technology. We are RAPIDLY approaching civil war in this country due to the government's refusal to operate within its constitutional limits. Gun control is unconstitutional. (more)

5. What specific enhancements will be released with GigE Vision 2.0
Vincent Rowley, vice-chair of the GigE Vision Committee and contributing member of the GenICam Committee; system architect , Pleora Technologies Inc. This interactive technical presentation is intended to build on the information shared through an earlier webcast, titled Gigabit Ethernet and the GigE Vision Standard (available on demand) . It is geared to meet the needs of engineers and system integrators already familiar with video-over-Ethernet and the role of the GigE Vision standard. Vincent Rowley vice-chair of the GigE Vision Committee and contributing member of the GenICam Committee; system architect Pleora Technologies Inc. Point Grey Research : Point Grey Research, Inc. is a worldwide leader in the development of advanced digital camera technology products for machine vision, industrial imaging, and computer vision applications. (more)

UB's Srihari Wins Major International Computer Science Award
His speech, entitled "Probabilistic Graphical Models in Machine Learning," focused on the design of computer programs that learn and are able to modify their behavior in an environment of constantly changing information. Machine learning is crucial in fields such as document analysis and recognition due to the difficulty of expressing perceptual images, such as handwriting, in algorithms that computers can understand. Many second-generation machine learning programs were enabled by postal data collected at the Buffalo post office by UB CEDAR students. " Research by Srihari, his colleagues and students at CEDAR that allowed machines to recognize and understand handwriting was at the core of the first handwritten address-interpretation system used by the U.S. (more)

Researchers Develop World's First Robot System to Dress Elderly and Physically ...
The Nara Institute of Science and Technology (NAIST), a national corporation university in Japan, has developed the worlds first robotic system that can learn to dress the physically challenged as well as aged persons. According to Associate Professor, Tomohiro Shibata from the Mathematical Informatics Laboratory who also headed the research, Barrett's robots are incomparable because they are able to respond to even the slightest physical interactions, implementing the algorithms to learn to clothe people. The group led by Associate Professor Tomohiro Shibata, in response to the key technical problem has created a dual-arm robotic system, which can learn clothing-assistance motions after just a few trials. (more)

Parascript Announces AccuDetect 5.0, Computer Aided Detection for Digital ...
Accu Detect is tuned to work with the leading Full Field Digital Mammography (FFDM) and Computed Radiography (CR) systems. Available for FFDM vendors interested in reducing false-positive rates of existing CAD systems, Accu Detect is intended to assist radiologists in the early detection of breast cancer during mammography screening exams. The technology has been developed using a broad database of digital images from leading digital mammography systems. It uses several proprietary complementary algorithms to detect the presence of suspicious lesions on mammogram images. (more)

Integration Insights
How 2-D vision is used in conjunction with a robot National Instruments is a measurement and automation company and leading machine vision and scientific imaging tools provider. For over 15 years, National Instruments has provided hardware and software tools for imaging applications, including NI Smart Cameras, Embedded Vision Systems, Frame Grabbers, interactive configurable software, and flexible programming libraries that include hundreds of advanced image processing functions. An unmatched range of vision inspection, guidance and identification solutions along with our unique global network of vision specialists make Cognex the largest and most successful vision company in the world. is a worldwide leader in the development of advanced digital camera technology products for machine vision, industrial imaging, computer vision, and traffic and surveillance applications. (more)

Riverain gets FDA approval of lung cancer-detection software
Riverain Technologies has received regulatory approval for the next-generation version of its imaging software that suppresses bones to help radiologists detect cancerous lung nodules. Advertisement The new version of the software offers greater sensitivity, meaning it can better detect nodules, and better specificity, meaning it yields fewer false positives, said Steve Worrell, Riverains chief technology officer. A recent study of the new version of OnGuard by researchers at University of Chicago Medical Center found that the software identified 25 percent more lung cancers than radiologists found when reviewing the same X-rays without OnGuard. Brandon Glenn MedCity News Brandon Glenn is the Ohio bureau chief for MedCity News. (more)

Why Do Some People Never Forget A Face?
Individuals who process faces more holisticallythat is, as an integrated wholeare better at face recognition, says Liu.The findings will appear in an upcoming issue of Psychological Science, a journal published by the Association for Psychological Science. To isolate holistic processing as the key to face recognition, the researchers first measured the ability of study participants337 male and female studentsto remember whole faces, using a task in which they had to select studied faces and flowers from among unfamiliar ones. They do better when the feature is presented within the whole face than when it stands on its own among other noses: again, we remember the nose integrated into the whole face. But there was no link between facial recognition and general intelligence, which is made up of various cognitive processesa suggestion that face processing is unique. (more)

Integration Insights: Machine Vision for Factory Automation
- What are the important software algorithms used to perform image analysis and how they can be implemented - How to analyze specific applications and develop specifications with appropriate metrics to assist you in understanding and analyzing your own requirements With thousands of installations and 25 years of industry experience, the company is headquartered in Waterloo, Canada with operations in Montreal, Vancouver, Boston, Beijing, China, Eindhoven, the Netherlands; is a worldwide leader in the development of advanced digital camera technology products for machine vision, industrial imaging, computer vision and traffic and surveillance applications. Matrox Imaging : Established in 1976, Matrox Imaging is a leading developer of component-level solutions for machine vision, image analysis, medical imaging, and video surveillance. (more)

DARPA Shredder Challenge
DARPA has just released five puzzles in a contest that involves extracting information from shredded documents. The background for its Shredder Challenge is that troops often confiscate the remnants of destroyed documents in war zones, but reconstructing them is difficult - and it is a problem that computer science and artificial intelligence could be expected to help with. The ability to reconstruct shredded documents will potentially yield information that may save lives or offer critical information about an adversarys plans, said Dan Kaufman, Director of DARPAs Information Innovation Office. The Shredder Challenge is composed of five separate problems in which the number of documents, subject matter and the method of shredding is varied to present challenges of increasing difficulty. (more)

Robot vision lags behind human sight
Related articles NEW YORK: By pitting human vision against that of machines for the first time, computer scientists have shown that machines still struggle with interpreting visual patterns, compared to their human counterparts. The study, published in Proceedings of the National Academy of Sciences today, implies that computer vision research still has a long way to go before machines have visual perception rivalling that of humans. Humans recollect, robots forget The fact that humans are better than machines at understanding visual patterns did not come as a surprise to scientists. For machines to achieve a higher level of visual understanding, artificial intelligence researchers are teaching them to recognise individual parts of an object and combine their relative positions into a recognisable whole object. (more)

Image search will shape the future
But Peter Linseley, product manager of Google image search, says Google could come to the rescue again. Instead of typing text, simply take a picture from your mobile phone, do a search by image and the contents --the menu or the street signs or whatever text is in it -- will be translated for you. And as we increasingly replace traditional text with images, 3-D images and real time video in the next few years, it is hard to think of something which the humble field of image processing will not alchemise with its Midas touch. Simply put, image processing is a technical analysis of the complex aspects of an image, deploying algorithms. (more)

Silicon Valley's Kinect Contributions
I got very excited about those demos, Budiu says, and I kept telling everyone how cool they were. Budiu, a researcher at Microsoft Research Silicon Valley, soon got his chance. When Williams approached Budiu in July 2009 about solving some of the challenges with the project, Budiu jumped at the opportunity. I said, Yes, absolutely, Budiu says, and I put everything else I was doing on hold. (more)

Computer Chronicles: Artificial Intelligence (1986).
What is Artificial Intelligence? Does AI even exist? These are just two of the questions addressed in this episode. Topics covered include expert systems, machine vision, decision support software, natural languageprocessing, and speech recognition systems. Hosted by Stuart Cheifet and Gary Kildall, with commentary from George Morrow. Guests: Hubert Dreyfus, UC Berkeley; Gary Hendrix, Symantec; S. Jerrold Kaplan, Lotus Development; Harry Tennant, Texas Instruments; and Terry Winograd, Stanford University. January 2, 1986. (more)

Computer Chronicles: Computers and the Pentagon - Part One (1986).
"The world's biggest computer user is the U.S. government and the military in particular. This program reviews the military uses of computer technology. Shot on location at various research centers around the country. Featured are the Robotics Institute at Carnegie Mellon University in Pittsburgh, Pennsylvania and Advanced Decision Systems in Mountain View, California. Demonstrations include the Pilot's Assistant and the Battlefield Commander's Assistant. Also flight and battle simulators from Singer Link." 1986. (more)

Computer Chronicles: Neural Networks.
"Neural networks are artificial intelligence systems modeled after the human brain. This program looks at several examples and applications. Included are Braincel 1.1 from Promised Land Technologies [demonstrated by Murray Ruggiero], BrainMaker Professional 2.0 from California Scientific Software [demonstrated by Mark Lawrence], MacBrain 3.0 from Neurix [demonstrated by Matt Jensen], NeuroSMARTS from Cognition Technology [demonstrated by Richard Mansfield], and ExploreNet from HNC. Also includes visits to NASA [Max Reid describes HONN: Higher Order Neural Network] and Intel [Mark Holler describes ETANN: Electronically Trainable Analog Neural Network] to see the work they're doing on neural networks." Also appearing on the show is Tom J. Schwartz (The Schwartz Assoc.). Hosted by Stewart Cheifet and Jan Lewis. May 15, 1991. (more)

DARPA LAGR Program: Learning Applied to Long-Range Vision using a Collision-Free Navigation Platform.
New York University uses machine learning to extend the vision of the DARPA LAGR robots. July 14, 2008. (more)

Discussion of and Demonstrations of Learning Programs for Robots.
The first half of the film is a lecture by Marvin Minsky describing the basic ideas of Patrick Winston's learning program, using examples and "near misses" to refine the program's model of what an "arch" is. The second half of the film is a narration by Dave Waltz describing other robotics research at MIT. He discusses Tim Finin's program that uses Winston-like models to recognize objects that match the model even when parts of the object are obscured. It uses hypotheses about dimensions of the objects that it can not directly observe. 1975??. (more)

NASA’s Mars Exploration Rover Video Gallery: Rover Navigation 101 - Autonomous Rover Navigation animation.
Simple explanation of Mars Exploration Rovers' autonomous driving software. January 13, 2004. (more)

Scientific American Frontiers with Alan Alda: "Almost Human" segment from the "Robots Alive!" broadcast.
Rodney Brooks is beginning to build the first robot with human-like senses, allowing it to learn about the world for itself, like a human baby. April 9, 1997. (more)

Scientific American Frontiers with Alan Alda: "Look, No Hands!" segment from the "Robots Alive!" broadcast.
Computer engineers are developing autonomous cars that can follow the highway and avoid obstacles without human intervention. April 9, 2007. (more)

Scientific American Frontiers with Alan Alda: "Mazes and Squiggles" segment from the "Robots Alive!" broadcast.
The world’s best robots compete to navigate mazes and chase after tennis balls, to test out the latest artificial intelligence programs. April 9, 1997. (more)

Scientific American Frontiers with Alan Alda: "World Cup for Robots" segment from the "Games Machines Play" broadcast.
Teams of robots programmed for soccer go head-to-head in RoboCup 2001. May 21, 2001. (more)

Scientific American Frontiers with Alan Alda: Cars That Think.
3 segments: Part 1 - Watch the Road. Alan rides in a vehicle that recognizes road signs and hazards – and warns the driver to slow down. Part 2 - Hold the Phone! Alan 'drives' the Ford VIRTTEX simulator that researchers use to investigate how distractions like cell phone calls or drowsiness affect driver safety. Part 3 - Smart Passenger. A virtual smart passenger named Sally listens in to the driver's speech at all times and responds appropriately. January 26, 2005. (more)

Searching with an Autononmous Robot.
We discuss online strategies for visibility-based searching for an object hidden behind a corner, using Kurt3D, a real autonomous mobile robot. This task is closely related to a number of well-studied problems. Our robot uses a three-dimensional laser scanner in a stop, scan, plan, go fashion for building a virtual three-dimensional environment. Besides planning trajectories and avoiding obstacles, Kurt3D is capable of identifying objects like a chair. We derive a practically useful and asymptotically optimal strategy that guarantees a competitive ratio of 2, which differs remarkably from the well-studied scenario without the need of stopping for surveying the environment. Our strategy is used by Kurt3D.. June 11, 2004. (more)

The Painting Fool.
Simon Colton lecture on The Painting Fool. Winner of the 2007 Machine Intelligence Competition. December 11, 2007. (more)

Visual Elements in Robotics: excerpt from "AI: What Can it Do? Where is it Going?".
Excerpt from lecture by Herbert A. Simon. March 21, 1990. (more)

Wired Science: Face Reader.
"Ziya Tong meets children with Asperger’s Syndrome testing a new MIT Media Lab device that reads facial expressions." In the course of the report she discusses the project with several individuals including Rana el Kaliouby Ph.D. (Mindreader Software Developer, MIT)); Alea Teeters (MIT Affective Computing Group), and Rosalind Picard, Ph.D. (Director, MIT Affective Computing Group). October 3, 2007. (more)