Videos Extract Mechanical Properties Of Liquid-gel Interfaces

Daily Science Journal (Jan. 29, 2008) — Blood coursing through vessels, lubricated cartilage sliding against joints, ink jets splashing on paper--living and nonliving things abound with fluids meeting solids. However important these liquid/solid boundaries may be, conventional methods cannot measure basic mechanical properties of these interfaces in their natural environments. Now, researchers at the National Institute of Standards and Technology (NIST) and the University of Minnesota have demonstrated a video method that eventually may be able to make measurements on these types of biological and industrial systems.*

Microscopic beads embedded in a gel surface were used to trace the motion of a gel forming an interface with a liquid. As the gel/liquid interface was stirred, the beads followed a complicated trajectory (patterns above photos), which the researchers broke down into a range of small, fast movements to large, slow movements in order to determine the gel's underlying mechanical properties. As the strength of the flow is increased (from left to right), the scale of the motion increases. (Credit: NIST)

Optical microrheology--an emerging tool for studying flow in small samples--usually relies on heat to stir up motion. Analyzing this heat-induced movement can provide the information needed to determine important mechanical properties of fluids and the interfaces that fluids form with other materials. However, when strong flows overwhelm heat-based motion, this method isn't applicable.

Motivated by this, researchers developed a video method that can extract optically basic properties of the liquid/solid interface in strong flows. The solid material they chose was a gel, a substance that has both solid-like properties such as elasticity and liquid-like properties such as viscosity (resistance to flow).

In between a pair of centimeter-scale circular plates, the researchers deposited a gel of polydimethylsiloxane (a common material used in contact lenses and microfluidics devices). Pouring a liquid solution of polypropylene glycol on the gel, they then rotated the top plate to create forces at the liquid/gel interface. The results could be observed by tracking the motion of styrene beads in the gel.

The researchers discovered that the boundary between the liquid and gel became unstable in response to "mechanical noise" (irregularities in the motion of the plates). Such "noise" occurs in real-world physical systems. Surprisingly, a small amount of this mechanical noise produced a lot of motion at the fluid/gel interface. This motion provided so much useful information that the researchers could determine the gel's mechanical properties--namely its "viscoelasticity"--at the liquid/gel interface.

The encouraging results from this model system show that this new approach could potentially be applied to determining properties of many useful and important liquid/solid interfaces. The NIST/Minnesota approach has possible applications in areas as diverse as speech therapy where observing the flow of air over vocal cords could enable noninvasive measures of vocal tissue elasticity and help clinicians detect problems at an early stage. Also, this research may help clarify specific plastics manufacturing problems, such as "shear banding," in which flow can separate a uniformly blended polymer undesirably into different components.

* E.K. Hobbie, S. Lin-Gibson, and S. Kumar Non-Brownian microrheology of a fluid-gel interface, To appear in Physical Review Letters.

Adapted from materials provided by National Institute of Standards and Technology.

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New Experimental Website Converts Photos Into 3D Models

Daily Science Journal (Jan. 28, 2008) — An artist might spend weeks fretting over questions of depth, scale and perspective in a landscape painting, but once it is done, what's left is a two-dimensional image with a fixed point of view. But the Make3d algorithm, developed by Stanford computer scientists, can take any two-dimensional image and create a three-dimensional "fly around" model of its content, giving viewers access to the scene's depth and a range of points of view.

Maui coast, Hawaii. A new program created by Stanford computer scientists, can take any two-dimensional image and create a three-dimensional "fly around" model of its content, giving viewers access to the scene's depth and a range of points of view. (Credit: Michele Hogan)

"The algorithm uses a variety of visual cues that humans use for estimating the 3-D aspects of a scene," said Ashutosh Saxena, a doctoral student in computer science who developed the Make3d website with Andrew Ng, an assistant professor of computer science. "If we look at a grass field, we can see that the texture changes in a particular way as it becomes more distant."

The applications of extracting 3-D models from 2-D images, the researchers say, could range from enhanced pictures for online real estate sites to quickly creating environments for video games and improving the vision and dexterity of mobile robots as they navigate through the spatial world.

Extracting 3-D information from still images is an emerging class of technology. In the past, some researchers have synthesized 3-D models by analyzing multiple images of a scene. Others, including Ng and Saxena in 2005, have developed algorithms that infer depth from single images by combining assumptions about what must be ground or sky with simple cues such as vertical lines in the image that represent walls or trees. But Make3d creates accurate and smooth models about twice as often as competing approaches, Ng said, by abandoning limiting assumptions in favor of a new, deeper analysis of each image and the powerful artificial intelligence technique "machine learning."

Restoring the third dimension

To "teach" the algorithm about depth, orientation and position in 2-D images, the researchers fed it still images of campus scenes along with 3-D data of the same scenes gathered with laser scanners. The algorithm correlated the two sets together, eventually gaining a good idea of the trends and patterns associated with being near or far. For example, it learned that abrupt changes along edges correlate well with one object occluding another, and it saw that things that are far away can be just a little hazier and more bluish than things that are close.

To make these judgments, the algorithm breaks the image up into tiny planes called "superpixels," which are within the image and have very uniform color, brightness and other attributes. By looking at a superpixel in concert with its neighbors, analyzing changes such as gradations of texture, the algorithm makes a judgment about how far it is from the viewer and what its orientation in space is. Unlike some previous algorithms, the Stanford one can account for planes at any angle, not just horizontal or vertical. This allows it to create models for scenes that have planes at many orientations, such as the curved branches of trees or the slopes of mountains.

On the Make3d website, the algorithm puts images uploaded by users into a processing queue and will send an e-mail when the model has been rendered. Users can then vote on whether the model looks good, and can see an alternative rendering and even tinker with the model to fix what might not have been rendered right the first time.

Photos can be uploaded directly or pulled into the site from the popular photo-sharing site Flickr.

Although the technology works better than any other has so far, Ng said, it is not perfect. The software is at its best with landscapes and scenery rather than close-ups of individual objects. Also, he and Saxena hope to improve it by introducing object recognition. The idea is that if the software can recognize a human form in a photo it can make more accurate distance judgments based on the size of the person in the photo.

A paper on the algorithm by Ng, Saxena and a fellow student, Min Sun, won the best paper award at the 3-D recognition and reconstruction workshop at the International Conference on Computer Vision in Rio de Janeiro in October 2007.

For many panoramic scenes, there is still no substitute for being there. But when flat photos become 3-D, viewers can feel a little closer—or farther. The algorithm runs at http://make3d.stanford.edu.

Adapted from materials provided by Stanford University.

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Face Recognition Made Possible With New Computer Program

Daily Science Journal (Jan. 25, 2008) — Hung-Son Le, Umeå University, Sweden, has developed in his PhD dissertation the algorithms that give a computer the possibility of recognising a face, even if only one picture is taken. The results can be used for safe and secure identity control or, on the light side, to find out to which famous persons you look alike.

If a non-authorised person gets access to your pin code and credit card, most likely your money will disappear from your account. Nevertheless, this would be impossible if the ATM could recognize your face as you look at a camera. Now, the algorithms to carry out this function, face recognition, exist. Face recognition can also be used in other functions, for instance in a dating service. Maybe the customer is interested in a man that looks like Brad Pitt or a woman that looks like Angelina Jolie.

Systems that can identify different faces are normally trained through a database with a large collection of face images in different illumination and pose. Nevertheless to collect such a large number of face images for each person is difficult and quite often expensive. Moreover these systems have problems due to the bad quality of the pictures, as well as facial expressions, the variety of angles and the different illuminations. These problems are now over.

The effective algorithms developed by Hung-Son Le make it possible to have a system that can identify a face even when there is only one picture in the database for each person. Moreover, the effectiveness of the system is a considerable improvement when taking into account light conditions, or facial expressions. His algorithms use a method than improves contrast in underexposed and overexposed pictures. Thus details can be made visible which otherwise would be difficult for a computer to identify. Given the method used (Hidden Markov Model, HMM), once the system is in place, it needs no time for retraining, when compared to existing HMM-based competitors, to “know” new pictures with different expressions taken under different illumination conditions.

The experiments carried out with the system and tested against international standards such as FERET and the Yale database, have demonstrated that it outperforms the leading competitors.

Commercial applications based on the PhD dissertation results are under development and will soon be presented. Among others, a face websearch engine is under final development phase.

Adapted from materials provided by Umeå University, via AlphaGalileo.

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Color Sudoku Puzzle Demonstrates New Vision For Computing

Daily Science Journal (Dec. 20, 2007) — Researchers at the University of Warwick's Department of Computer Science have developed a colour based Sudoku Puzzle that will help Sudoku players solve traditional Sudoku puzzles but also helps demonstrate the potential benefits of a radical new vision for computing.

The colour Sudoku adds another dimension to solving the puzzle by assigning a colour to each digit. Squares containing a digit are coloured according to the digit's colour. Empty squares are coloured according to which digits are possible for that square taking account of all current entries in the square's row, column and region. The empty square's colour is the combination of the colours assigned to each possible digit. This gives players major clues as darker coloured empty squares imply fewer number possibilities.

More usefully an empty square that has the same colour as a completed square must contain the same digit. If a black square is encountered then a mistake has been made. Players also can gain additional clues by changing the colour assigned to the each digit and watching the unfolding changes in the pattern of colours.

Sudoku players can test this for themselves at: http://www.warwick.ac.uk/go/sudoku. (NB page requires Flash 9)

However the colour Sudoku is more than just a game to the University of Warwick Computer Scientists. For doctoral researcher Antony Harfield it is a way of exploring how logic and perception interact using a radical approach to computing called Empirical Modelling. The method can be applied to other creative problems and he is exploring how this experimental modelling technique can be used in educational technology and learning.

The interplay between logic and perception, as it relates to interactions between computers and humans is viewed as key to the building of better software. It is of particular relevance for artificial intelligence, computer graphics, and educational technology. The interaction between the shifting colour squares and the logical deductions of the Sudoku puzzle solver is a good illustration of the unusual quality of this "Empirical Modelling" approach.

Previously the researchers have been able to use their principles to analyse a railway accident in the Clayton Tunnel near Brighton when the telegraph was introduced in 1861. Reports at the time sought to blame various railway personnel but by applying Empirical Modelling the researchers have created an environment in which experimenters can replay the roles of the drivers, signalmen and other personnel involved. This has shown that there were systemic problems arising from the introduction of the new technology.

Dr Steve Russ of the Empirical Modelling group at the University of Warwick said: "Traditional computer programs are best-suited for tasks that are so well-understood they can, without much loss, be expressed in a closed, mechanical form in which all interactions or changes are 'pre-planned'. Even in something so simple as a Sudoku puzzle humans use a mixture of perception, expectation, experience and logic that is just incompatible with the way a computer program would typically solve the puzzle. For safety-critical systems (such as railway management) it is literally a matter of life and death that we learn to use computers in ways that integrate smoothly with human perception, communication and action. This is our goal with Empirical Modelling."

Adapted from materials provided by University of Warwick, via EurekAlert!, a service of AAAS.

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Computers Learn Art Appreciation

Daily Science Journal (Nov. 5, 2007) — A new mathematical program developed in the Department of Computer Sciences at the University of Haifa will enable computers to "know" if the artwork you are looking at is a Leonardo da Vinci original, as the seller claims, or by another less well known artist. "The field of computer vision is very complex and multifaceted. We hope that our new development is another step forward in this field," said Prof. Daniel Keren who developed the program.

Through this innovation, the researchers "taught" the computer to identify the artworks of different artists. The computer learned to identify the artists after the program turned the drawings of nature, people, flowers and other scenes to a series of mathematical symbols, sines and cosines. After the computer "learns" some of the works of each artist, the program enables the computer to master the individual style of each artist and to identify the artist when looking at other works -- works the computer has never seen.

According to Prof. Keren, the program can identify the works of a specific artist even if they depict different scenes. "As soon as the computer learns to recognize the clock drawings of Dali, it will recognize his other paintings, even without clocks. As soon as the computer learns to recognize the swirls of Van Gogh, it will recognize them in pictures it has never seen before."

This new development is a step forward in the field of computer vision. According to Prof. Keren, this field is still inferior to human vision. "Human vision has undergone evolution of millions of years and our field is only 30 years old. At this stage computers still have difficulty doing things that are very simple for people, for example, recognizing a picture of a human face. A computer has difficulty identifying when a picture is of a human face or how many faces are in a picture. However, computers are very good at simulating and sketching 3 dimensional images like the arteries in the brain or a road network."

At present, the new program can be helpful to someone who appreciates art, but not to a real expert in the field. If you are a novice who paid a hefty price for a picture that the seller claimed is an exact copy of a Da Vinci, the program can tell you if you wasted your money or made a smart purchase.

Adapted from materials provided by University of Haifa.

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The New Face Of Identity Protection: You

Daily Science Journal (Aug. 5, 2007) — Trying to remember dozens of personal identification numbers (PIN), passwords and credit card numbers may not be necessary for much longer, thanks to a University of Houston professor and his team.

Taking a radically new approach, UH Eckhard Pfeiffer Professor Ioannis Kakadiaris and his Computational Biomedicine Lab (CBL) developed the URxD face recognition software that uses a three-dimensional snapshot of a person’s face to create a unique identifier, a biometric. Shown in government testing to be tops in its field, URxD can be used for everything from gaining access to secure facilities to authorizing credit card purchases. The identification procedure is as effortless as taking a photograph.

URxD leads the pack for 3D face recognition solutions based on the face’s shape, according to the results of the Face Recognition Vendor Test (FRVT 2006). The National Institute of Standards and Technology conducted the rigorous testing for FRVT 2006, which was sponsored by several U.S. government agencies. FRVT 2006 is the first independent performance benchmark for 3-D face recognition technology.

“Accuracy is the name of the game in 3-D face recognition,” Kakadiaris said. “What makes our system so accurate is the strength of the variables that we use to describe a person’s face.

“Remembering dozens of personal identification numbers and passwords is not the solution to identity theft. PINs and passwords are not only inconvenient to memorize, but also are impractical to safeguard. In essence, they merely tie two pieces of information together; once the secret is compromised, the rest follows. The solution is to be able to tie your private information to your person in a way that cannot be compromised.”

The software and technology also could play a role in national security.

“With the growing concern for security at the personal, national and international level, the University of Houston is pleased that Dr. Kakadiaris and his team have demonstrated a very promising technology for personal identification,” said John Warren, UH associate general counsel for research and intellectual property management. “We look forward to its adoption by government and industry.”

URxD inventors are hoping for corporate interest in bringing the technology, now at the advanced prototype stage, to the marketplace.

“This technology will have a positive impact on some of today’s hottest issues,” Kakadiaris said. “Imagine a day when you simply sit in front of your computer, and it recognizes who you are. Everything will be both easier and more secure, from online purchases to parental control of what Web sites your children can visit.”

Note: Use of results from the Facial Recognition Vendor Test 2006 does not constitute the U.S. government’s endorsement of any particular system.

Adapted from materials provided by University of Houston.

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