Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Computational model sheds light on how the brain recognizes objects

09.06.2010
Could help advance artificial-intelligence research

Researchers at MIT’s McGovern Institute for Brain Research have developed a new mathematical model to describe how the human brain visually identifies objects. The model accurately predicts human performance on certain visual-perception tasks, which suggests that it’s a good indication of what actually happens in the brain, and it could also help improve computer object-recognition systems.

The model was designed to reflect neurological evidence that in the primate brain, object identification — deciding what an object is — and object location — deciding where it is — are handled separately. “Although what and where are processed in two separate parts of the brain, they are integrated during perception to analyze the image,” says Sharat Chikkerur, lead author on a paper appearing this week in the journal Vision Research, which describes the work. “The model that we have tries to explain how this information is integrated.”

The mechanism of integration, the researchers argue, is attention. According to their model, when the brain is confronted by a scene containing a number of different objects, it can’t keep track of all of them at once. So instead it creates a rough map of the scene that simply identifies some regions as being more visually interesting than others. If it’s then called upon to determine whether the scene contains an object of a particular type, it begins by searching — turning its attention toward — the regions of greatest interest.

Chikkerur and Tomaso Poggio, the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences and at the Computer Science and Artificial Intelligence Laboratory, together with graduate student Cheston Tan and former postdoc Thomas Serre, implemented the model in software, then tested its predictions against data from experiments with human subjects. The subjects were asked first to simply regard a street scene depicted on a computer screen, then to count the cars in the scene, and then to count the pedestrians, while an eye-tracking system recorded their eye movements. The software predicted with great accuracy which regions of the image the subjects would attend to during each task.

The software’s analysis of an image begins with the identification of interesting features — rudimentary shapes common to a wide variety of images. It then creates a map that depicts which features are found in which parts of the image. But thereafter, shape information and location information are processed separately, as they are in the brain.

The software creates a list of all the interesting features in the feature map, and from that, it creates another list, of all the objects that contain those features. But it doesn’t record any information about where or how frequently the features occur.

At the same time, it creates a spatial map of the image that indicates where interesting features are to be found, but not what sorts of features they are.

It does, however, interpret the “interestingness” of the features probabilistically. If a feature occurs more than once, its interestingness is spread out across all the locations at which it occurs. If another feature occurs at only one location, its interestingness is concentrated at that one location.

Mathematically, this is a natural consequence of separating information about objects’ identity and location and interpreting the results probabilistically. But it ends up predicting another aspect of human perception, a phenomenon called “pop out.” A human subject presented with an image of, say, one square and one star will attend to both objects about equally. But a human subject presented an image of one square and a dozen stars will tend to focus on the square.

Like a human asked to perform a visual-perception task, the software can adjust its object and location models on the fly. If the software is asked to identify only the objects at a particular location in the image, it will cross off its list of possible objects any that don’t contain the features found at that location.

By the same token, if it’s asked to search the image for a particular kind of object, the interestingness of features not found in that object will go to zero, and the interestingness of features found in the object will increase proportionally. This is what allows the system to predict the eye movements of humans viewing a digital image, but it’s also the aspect of the system that could aid the design of computer object-recognition systems. A typical object-recognition system, when asked to search an image for multiple types of objects, will search through the entire image looking for features characteristic of the first object, then search through the entire image looking for features characteristic of the second object, and so on. A system like Poggio and Chikkerur’s, however, could limit successive searches to just those regions of the image that are likely to have features of interest.

Source: “What and where: A Bayesian inference theory of attention.” Sharat S. Chikkerur, Thomas Serre, Cheston Tan, Tomaso Poggio. Vision Research. Week of 7 June, 2010.

Funding: DARPA, the Honda Research Institute USA, NEC, Sony and the Eugene McDermott Foundation

Jennifer Hirsch | EurekAlert!
Further information:
http://www.mit.edu

More articles from Interdisciplinary Research:

nachricht Lego-like wall produces acoustic holograms
17.10.2016 | Duke University

nachricht New evidence on terrestrial and oceanic responses to climate change over last millennium
11.10.2016 | University of Granada

All articles from Interdisciplinary Research >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: Significantly more productivity in USP lasers

In recent years, lasers with ultrashort pulses (USP) down to the femtosecond range have become established on an industrial scale. They could advance some applications with the much-lauded “cold ablation” – if that meant they would then achieve more throughput. A new generation of process engineering that will address this issue in particular will be discussed at the “4th UKP Workshop – Ultrafast Laser Technology” in April 2017.

Even back in the 1990s, scientists were comparing materials processing with nanosecond, picosecond and femtosesecond pulses. The result was surprising:...

Im Focus: Shape matters when light meets atom

Mapping the interaction of a single atom with a single photon may inform design of quantum devices

Have you ever wondered how you see the world? Vision is about photons of light, which are packets of energy, interacting with the atoms or molecules in what...

Im Focus: Novel silicon etching technique crafts 3-D gradient refractive index micro-optics

A multi-institutional research collaboration has created a novel approach for fabricating three-dimensional micro-optics through the shape-defined formation of porous silicon (PSi), with broad impacts in integrated optoelectronics, imaging, and photovoltaics.

Working with colleagues at Stanford and The Dow Chemical Company, researchers at the University of Illinois at Urbana-Champaign fabricated 3-D birefringent...

Im Focus: Quantum Particles Form Droplets

In experiments with magnetic atoms conducted at extremely low temperatures, scientists have demonstrated a unique phase of matter: The atoms form a new type of quantum liquid or quantum droplet state. These so called quantum droplets may preserve their form in absence of external confinement because of quantum effects. The joint team of experimental physicists from Innsbruck and theoretical physicists from Hannover report on their findings in the journal Physical Review X.

“Our Quantum droplets are in the gas phase but they still drop like a rock,” explains experimental physicist Francesca Ferlaino when talking about the...

Im Focus: MADMAX: Max Planck Institute for Physics takes up axion research

The Max Planck Institute for Physics (MPP) is opening up a new research field. A workshop from November 21 - 22, 2016 will mark the start of activities for an innovative axion experiment. Axions are still only purely hypothetical particles. Their detection could solve two fundamental problems in particle physics: What dark matter consists of and why it has not yet been possible to directly observe a CP violation for the strong interaction.

The “MADMAX” project is the MPP’s commitment to axion research. Axions are so far only a theoretical prediction and are difficult to detect: on the one hand,...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

Event News

ICTM Conference 2017: Production technology for turbomachine manufacturing of the future

16.11.2016 | Event News

Innovation Day Laser Technology – Laser Additive Manufacturing

01.11.2016 | Event News

#IC2S2: When Social Science meets Computer Science - GESIS will host the IC2S2 conference 2017

14.10.2016 | Event News

 
Latest News

Simple processing technique could cut cost of organic PV and wearable electronics

06.12.2016 | Materials Sciences

3-D printed kidney phantoms aid nuclear medicine dosing calibration

06.12.2016 | Medical Engineering

Robot on demand: Mobile machining of aircraft components with high precision

06.12.2016 | Power and Electrical Engineering

VideoLinks
B2B-VideoLinks
More VideoLinks >>>