Researchers develop optimal algorithm for determining focus error in eyes and cameras

ScienceDaily (Sep. 26, 2011) — University of Texas at Austin researchers have discovered how to extract and use information in an individual image to determine how far objects are from the focus distance, a feat only accomplished by human and animal visual systems until now.

Like a camera, the human eye has an auto-focusing system, but human auto-focusing rarely makes mistakes. And unlike a camera, humans do not require trial and error to focus an object.

Johannes Burge, a postdoctoral fellow in the College of Liberal Arts' Center for Perceptual Systems and co-author of the study, says it is significant that a statistical algorithm can now determine focus error, which indicates how much a lens needs to be refocused to make the image sharp, from a single image without trial and error.

"Our research on defocus estimation could deepen our understanding of human depth perception," Burge says. "Our results could also improve auto-focusing in digital cameras. We used basic optical modeling and well-understood statistics to show that there is information lurking in images that cameras have yet to tap."

The researchers' algorithm can be applied to any blurry image to determine focus error. An estimate of focus error also makes it possible to determine how far objects are from the focus distance.

In the human eye, inevitable defects in the lens, such as astigmatism, can help the visual system (via the retina and brain) compute focus error; the defects enrich the pattern of "defocus blur," the blur that is caused when a lens is focused at the wrong distance. Humans use defocus blur to both estimate depth and refocus their eyes. Many small animals use defocus as their primary depth cue.

"We are now one step closer to understanding how these feats are accomplished," says Wilson Geisler, director of the Center for Perceptual Systems and coauthor of the study. "The pattern of blur introduced by focus errors, along with the statistical regularities of natural images, makes this possible."

Burge and Geisler considered what happens to images as focus error increases: an increasing amount of detail is lost with larger errors. Then, they noted that even though the content of images varies considerably (e.g. faces, mountains, flowers), the pattern and amount of detail in images is remarkably constant. This constancy makes it possible to determine the amount of defocus and, in turn, to re-focus appropriately.

Their article was recently published in the Proceedings of the National Academy of Sciences. The research was supported by a grant from the National Institutes of Health.

The Center for Perceptual Systems is an integrated program that overlaps several separate departments: Neuroscience, Psychology, Electrical and Computer Engineering, Neurobiology, Computer Science, and Speech and Communication.

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The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

J. Burge, W. S. Geisler. Optimal defocus estimation in individual natural images. Proceedings of the National Academy of Sciences, 2011; DOI: 10.1073/pnas.1108491108

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

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Researchers develop optimal algorithm for determining focus error in eyes and cameras

ScienceDaily (Sep. 26, 2011) — University of Texas at Austin researchers have discovered how to extract and use information in an individual image to determine how far objects are from the focus distance, a feat only accomplished by human and animal visual systems until now.

Like a camera, the human eye has an auto-focusing system, but human auto-focusing rarely makes mistakes. And unlike a camera, humans do not require trial and error to focus an object.

Johannes Burge, a postdoctoral fellow in the College of Liberal Arts' Center for Perceptual Systems and co-author of the study, says it is significant that a statistical algorithm can now determine focus error, which indicates how much a lens needs to be refocused to make the image sharp, from a single image without trial and error.

"Our research on defocus estimation could deepen our understanding of human depth perception," Burge says. "Our results could also improve auto-focusing in digital cameras. We used basic optical modeling and well-understood statistics to show that there is information lurking in images that cameras have yet to tap."

The researchers' algorithm can be applied to any blurry image to determine focus error. An estimate of focus error also makes it possible to determine how far objects are from the focus distance.

In the human eye, inevitable defects in the lens, such as astigmatism, can help the visual system (via the retina and brain) compute focus error; the defects enrich the pattern of "defocus blur," the blur that is caused when a lens is focused at the wrong distance. Humans use defocus blur to both estimate depth and refocus their eyes. Many small animals use defocus as their primary depth cue.

"We are now one step closer to understanding how these feats are accomplished," says Wilson Geisler, director of the Center for Perceptual Systems and coauthor of the study. "The pattern of blur introduced by focus errors, along with the statistical regularities of natural images, makes this possible."

Burge and Geisler considered what happens to images as focus error increases: an increasing amount of detail is lost with larger errors. Then, they noted that even though the content of images varies considerably (e.g. faces, mountains, flowers), the pattern and amount of detail in images is remarkably constant. This constancy makes it possible to determine the amount of defocus and, in turn, to re-focus appropriately.

Their article was recently published in the Proceedings of the National Academy of Sciences. The research was supported by a grant from the National Institutes of Health.

The Center for Perceptual Systems is an integrated program that overlaps several separate departments: Neuroscience, Psychology, Electrical and Computer Engineering, Neurobiology, Computer Science, and Speech and Communication.

Recommend this story on Facebook, Twitter,
and Google +1:

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Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

J. Burge, W. S. Geisler. Optimal defocus estimation in individual natural images. Proceedings of the National Academy of Sciences, 2011; DOI: 10.1073/pnas.1108491108

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

Model finds optimal fiber optic network connections 10,000 times more quickly

ScienceDaily (June 28, 2011) — Designing fiber optic networks involves finding the most efficient way to connect phones and computers that are in different places -- a costly and time-consuming process. Now researchers from North Carolina State University have developed a model that can find optimal connections 10,000 times more quickly, using less computing power to solve the problem.

"Problems that used to take days to solve can now be solved in just a few seconds," says Dr. George Rouskas, computer science professor at NC State and author of a paper describing the new method. The model could solve problems more than 10,000 times faster when data is routed through larger "rings," in the network, Rouskas says.

Every time you make a phone call or visit a website, you send and receive data in the form of wavelengths of light through a network of fiber optic cables. These data are often routed through rings that ensure the information gets where it needs to go. These ring networks are faced with the constant challenge of ensuring that their system design can meet user requirements efficiently. As a result, ring network designers try to determine the best fiber optic cable route for transmitting user data between two points, as well as which wavelength of light to use. Most commercial fiber optics handle approximately 100 different wavelengths of light.

Solving these design challenges is difficult and time-consuming. Using existing techniques, finding the optimal solution for a ring can take days, even for smaller rings. And a ring's connections are modified on an ongoing basis, to respond to changing use patterns and constantly increasing traffic demands.

But the new model developed by Rouskas and his team should speed things up considerably. Specifically, the researchers have designed a mathematical model that identifies the exact optimal routes and wavelengths for ring network designers. The model creates a large graph of all the paths in a ring, and where those paths overlap. The model then breaks that graph into smaller units, with each unit consisting of the paths in a ring that do not overlap. Because these paths do not overlap, they can use the same wavelengths of light. Paths that overlap cannot use the same wavelengths of light -- because two things cannot occupy the same space at the same time.

By breaking all of the potential paths down into these smaller groups, the model is able to identify the optimal path and wavelength between two points much more efficiently than previous techniques.

"This will significantly shorten the cycle of feedback and re-design for existing rings," Rouskas says. "It also means that the ring design work can be done using fewer computer resources, which makes it less expensive. This should allow network providers to be more responsive to user demands than ever before."

The paper, "Fast Exact ILP Decompositions for Ring RWA," is published in the July issue of the Journal of Optical Communications and Networking. The paper was co-authored by Dr. Emre Yetginer, a former postdoctoral researcher at NC State now at Tubitak UEKAE, and NC State Ph.D. student Zeyu Liu.

NC State's Department of Computer Science is part of the university's College of Engineering.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by North Carolina State University.

Journal Reference:

Emre Yetginer, Zeyu Liu, George N. Rouskas. Fast Exact ILP Decompositions for Ring RWA. Journal of Optical Communications and Networking, 2011; 3 (7): 577 DOI: 10.1364/JOCN.3.000577

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