Clear vision despite a heavy head: Model explains the choice of simple movements

ScienceDaily (Nov. 9, 2011) — In one respect, handling a computer mouse is just like looking in the rearview mirror: well established movements help the brain to concentrate on the essentials. But just a simple gaze shift to a new target bears the possibility of an almost infinite number of combinations of eye and head movement: how fast do we move eye and head? How much does the eye rotate, how much the head?

Until now, it was unclear why the brain chooses a particular movement option from the set of all possible combinations. A team led by Dr. Stefan Glasauer (LMU Munich), project leader at the Bernstein Center Munich, has now developed a mathematical model that accurately predicts horizontal gaze movements. Besides eye and head contribution to the gaze shift it also predicts movement duration and velocity.

In contrast to most previous models, the researchers considered the movement of head and eye to the target as well as the counter-movement of the eye after the gaze has reached the target, but the head is still moving. "The longer the movement, the more perturbations add up," says Glasauer. "However, the faster the movement, the more errors arise from acceleration and large muscle forces." On the basis of this information, the Munich researchers calculated eye and head movements and determined the movement combination that caused the fewest disturbances. This movement matched that chosen by healthy volunteers not only in natural conditions but also in an experiment where subjects' head movements were altered by an experimental increase in the head's rotational inertia.

These findings could help teach robots humanoid movements and thus facilitate interaction with service robots. It may also be helpful in the construction of "smart" prostheses. These devices could offer the carrier a choice of movements that come closest to the natural human ones. For the next step, Glasauer and colleagues want to examine three-dimensional eye-head movements and aim to better understand simple movement learning.

The Bernstein Center Munich is part of the National Bernstein Network Computational Neuroscience (NNCN) in Germany. The NNCN was established by the German Federal Ministry of Education and Research with the aim of structurally interconnecting and developing German capacities in the new scientific discipline of computational neuroscience. The network is named after the German physiologist Julius Bernstein (1835-1917).

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The above story is reprinted from materials provided by Ludwig-Maximilians-Universitaet Muenchen (LMU).

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Journal Reference:

Saglam M., Lehnen N., Glasauer S. Optimal control of natural eye-head movements minimizes the impact of noise.. J Neurosci., 31(45):16185%u201316193

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New generation of superlattice cameras add more 'color' to night vision

ScienceDaily (Oct. 20, 2011) — Recent breakthroughs have enabled scientists from the Northwestern University's Center for Quantum Devices to build cameras that can see more than one optical waveband or "color" in the dark. The semiconducting material used in the cameras -- called type-II superlattices -- can be tuned to absorb a wide range of infrared wavelengths, and now, a number of distinct infrared bands at the same time.

The idea of capturing light simultaneously at different wavelengths isn't new. Digital cameras in the visible spectrum are commonly equipped with detectors that sense red, green, and blue light to replicate a vast majority of colors perceived by the human eye. Multi-color detection in the infrared spectrum, however, offers unique functionalities beyond color representation. The resonant frequencies of compounds can often be found in this spectral range, which means that chemical spectroscopy can be relayed in images real-time.

"When coupled with image-processing algorithms performed on multiple wavebands, the amount of information rendered in a particular scene is tremendous," said Manijeh Razeghi, Walter P. Murphy Professor in Electrical Engineering and Computer Science at the McCormick School of Engineering and director of the Center for Quantum Devices.

Razeghi's group engineered the detection energies on the cameras to be extremely narrow, close to one-tenth of an electron volt, in what is known as the long-wave infrared window. Creating the cameras was difficult, however, because the light-absorbing layers are prone to parasitic effects. Furthermore, the detectors were designed to be stacked one on top of another, which provided spatially coincident pixel registration but added significantly to the growth and fabrication challenges. Nevertheless, a dual-band long-wave infrared 320-by-256 sized type-II superlattice camera was demonstrated for the first time in the world, the results of which were published in the July 2011 issue of Optics Letters.

Such infrared photon cameras based on another material called HgCdTe were used in disaster relief in March 2011 when a catastrophic tsunami damaged Japans' nuclear reactors. These cameras provided accurate temperature information about the reactors from unmanned aerial vehicles, providing officials the information they needed to orchestrate cooling efforts and prevent nuclear meltdown.

HgCdTe, however, is considered to be an expensive technology in the long-wave infrared due to its poor spectral uniformity and therefore yield -- areas in which type-II superlattices may prove more efficient.

"Type-II superlattices can be grown uniformly even at very long-wavelengths because its energy gap is determined by the alternating InAs and GaSb quantum well thicknesses, rather than its composition as is the case with HgCdTe," Razeghi said. The high-resolution multi-band type-II superlattice camera also offered very impressive performances, requiring only 0.5 milliseconds to capture a frame with temperature sensitivities as good as 0.015°C. "The high-performance, multi-functionality, and low cost offered by type-II superlattices truly make it an attractive infrared technology," she added.

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Journal Reference:

Edward Kwei-wei Huang, Abbas Haddadi, Guanxi Chen, Binh-Minh Nguyen, Minh-Anh Hoang, Ryan McClintock, Mark Stegall, Manijeh Razeghi. Type-II superlattice dual-band LWIR imager with M-barrier and Fabry–Perot resonance. Optics Letters, 2011; 36 (13): 2560 DOI: 10.1364/OL.36.002560

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Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

New generation of superlattice cameras add more 'color' to night vision

ScienceDaily (Oct. 20, 2011) — Recent breakthroughs have enabled scientists from the Northwestern University's Center for Quantum Devices to build cameras that can see more than one optical waveband or "color" in the dark. The semiconducting material used in the cameras -- called type-II superlattices -- can be tuned to absorb a wide range of infrared wavelengths, and now, a number of distinct infrared bands at the same time.

The idea of capturing light simultaneously at different wavelengths isn't new. Digital cameras in the visible spectrum are commonly equipped with detectors that sense red, green, and blue light to replicate a vast majority of colors perceived by the human eye. Multi-color detection in the infrared spectrum, however, offers unique functionalities beyond color representation. The resonant frequencies of compounds can often be found in this spectral range, which means that chemical spectroscopy can be relayed in images real-time.

"When coupled with image-processing algorithms performed on multiple wavebands, the amount of information rendered in a particular scene is tremendous," said Manijeh Razeghi, Walter P. Murphy Professor in Electrical Engineering and Computer Science at the McCormick School of Engineering and director of the Center for Quantum Devices.

Razeghi's group engineered the detection energies on the cameras to be extremely narrow, close to one-tenth of an electron volt, in what is known as the long-wave infrared window. Creating the cameras was difficult, however, because the light-absorbing layers are prone to parasitic effects. Furthermore, the detectors were designed to be stacked one on top of another, which provided spatially coincident pixel registration but added significantly to the growth and fabrication challenges. Nevertheless, a dual-band long-wave infrared 320-by-256 sized type-II superlattice camera was demonstrated for the first time in the world, the results of which were published in the July 2011 issue of Optics Letters.

Such infrared photon cameras based on another material called HgCdTe were used in disaster relief in March 2011 when a catastrophic tsunami damaged Japans' nuclear reactors. These cameras provided accurate temperature information about the reactors from unmanned aerial vehicles, providing officials the information they needed to orchestrate cooling efforts and prevent nuclear meltdown.

HgCdTe, however, is considered to be an expensive technology in the long-wave infrared due to its poor spectral uniformity and therefore yield -- areas in which type-II superlattices may prove more efficient.

"Type-II superlattices can be grown uniformly even at very long-wavelengths because its energy gap is determined by the alternating InAs and GaSb quantum well thicknesses, rather than its composition as is the case with HgCdTe," Razeghi said. The high-resolution multi-band type-II superlattice camera also offered very impressive performances, requiring only 0.5 milliseconds to capture a frame with temperature sensitivities as good as 0.015°C. "The high-performance, multi-functionality, and low cost offered by type-II superlattices truly make it an attractive infrared technology," she added.

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The above story is reprinted from materials provided by Northwestern University.

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Journal Reference:

Edward Kwei-wei Huang, Abbas Haddadi, Guanxi Chen, Binh-Minh Nguyen, Minh-Anh Hoang, Ryan McClintock, Mark Stegall, Manijeh Razeghi. Type-II superlattice dual-band LWIR imager with M-barrier and Fabry–Perot resonance. Optics Letters, 2011; 36 (13): 2560 DOI: 10.1364/OL.36.002560

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

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Computer vision: Music video by C-Mon & Kypski used for data collection

ScienceDaily (May 17, 2011) — Researchers at New York University's Courant Institute of Mathematical Sciences have adopted an innovative data collection method for their latest work in the area of computer vision -- a music video created by the Dutch progressive-electro band C-Mon & Kypski. Individual frames from the band's recent video for its song "More is Less" served as a unique visual database for the Courant researchers' work to develop computer vision technology.

Computer vision, a developing technology, aims to give eyesight to machines and is currently used in a range of applications. These include Microsoft's Kinect, which detects poses in order for game play to be controlled using only the body, and cell-phone technology that allows users to cash checks by merely snapping a picture.

However, for computer vision to truly mimic the human vision system, it must be able to reliably detect specific objects or individuals under a variety of conditions -- poor lighting, cluttered backgrounds, unusual clothing, and other sources of variation. In building such a system, developers have sought to implement an algorithm to perform "pose estimation" -- computer recognition of individuals or objects based on their positioning. However, in order for a computer to succeed at pose estimation it must draw from a large database of people or objects in a variety of poses -- after detecting a certain pose in its field of vision, it draws on its vast database of images to find a match.

"If we had many examples of people in similar pose, but under differing conditions, we could construct an algorithm that matches based on pose and ignores the distracting information -- lighting, clothing, and background," explained Graham Taylor, a post-doctoral fellow at the Courant Institute and one of the project's researchers. "But how do we collect such data?"

Departing from traditional data-collection methods, the team turned to Dutch progressive-electro band C-Mon & Kypski and, specifically, its video crowd-sourcing project--"One Frame of Fame" (http://oneframeoffame.com/)--which asks fans to replace one frame of the band's music video for the song "More or Less" with a capture from their webcams. In the project, a visitor to the band's website is shown a single frame of the video and asked to perform an imitation in front of the camera. The new contribution is spliced into the video that updates once an hour.

"This turned out to be the perfect data source for developing an algorithm that learns to compute similarity based on pose," explained Taylor, who obtained his doctorate in computer science from the University of Toronto. "Armed with the band's data and a few machine learning tricks up our sleeves, we built a system that is highly effective at matching people in similar pose but under widely different settings."

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The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by New York University, via EurekAlert!, a service of AAAS.

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Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Computer vision: Music video by C-Mon & Kypski used for data collection

ScienceDaily (May 17, 2011) — Researchers at New York University's Courant Institute of Mathematical Sciences have adopted an innovative data collection method for their latest work in the area of computer vision -- a music video created by the Dutch progressive-electro band C-Mon & Kypski. Individual frames from the band's recent video for its song "More is Less" served as a unique visual database for the Courant researchers' work to develop computer vision technology.

Computer vision, a developing technology, aims to give eyesight to machines and is currently used in a range of applications. These include Microsoft's Kinect, which detects poses in order for game play to be controlled using only the body, and cell-phone technology that allows users to cash checks by merely snapping a picture.

However, for computer vision to truly mimic the human vision system, it must be able to reliably detect specific objects or individuals under a variety of conditions -- poor lighting, cluttered backgrounds, unusual clothing, and other sources of variation. In building such a system, developers have sought to implement an algorithm to perform "pose estimation" -- computer recognition of individuals or objects based on their positioning. However, in order for a computer to succeed at pose estimation it must draw from a large database of people or objects in a variety of poses -- after detecting a certain pose in its field of vision, it draws on its vast database of images to find a match.

"If we had many examples of people in similar pose, but under differing conditions, we could construct an algorithm that matches based on pose and ignores the distracting information -- lighting, clothing, and background," explained Graham Taylor, a post-doctoral fellow at the Courant Institute and one of the project's researchers. "But how do we collect such data?"

Departing from traditional data-collection methods, the team turned to Dutch progressive-electro band C-Mon & Kypski and, specifically, its video crowd-sourcing project--"One Frame of Fame" (http://oneframeoffame.com/)--which asks fans to replace one frame of the band's music video for the song "More or Less" with a capture from their webcams. In the project, a visitor to the band's website is shown a single frame of the video and asked to perform an imitation in front of the camera. The new contribution is spliced into the video that updates once an hour.

"This turned out to be the perfect data source for developing an algorithm that learns to compute similarity based on pose," explained Taylor, who obtained his doctorate in computer science from the University of Toronto. "Armed with the band's data and a few machine learning tricks up our sleeves, we built a system that is highly effective at matching people in similar pose but under widely different settings."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by New York University, via EurekAlert!, a service of AAAS.

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here