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!
Investigating cell membranes: researchers develop a substance mimicking a vital membrane component
25.05.2018 | Westfälische Wilhelms-Universität Münster
New approach: Researchers succeed in directly labelling and detecting an important RNA modification
30.04.2018 | Westfälische Wilhelms-Universität Münster
The more electronics steer, accelerate and brake cars, the more important it is to protect them against cyber-attacks. That is why 15 partners from industry and academia will work together over the next three years on new approaches to IT security in self-driving cars. The joint project goes by the name Security For Connected, Autonomous Cars (SecForCARs) and has funding of €7.2 million from the German Federal Ministry of Education and Research. Infineon is leading the project.
Vehicles already offer diverse communication interfaces and more and more automated functions, such as distance and lane-keeping assist systems. At the same...
A research team led by physicists at the Technical University of Munich (TUM) has developed molecular nanoswitches that can be toggled between two structurally different states using an applied voltage. They can serve as the basis for a pioneering class of devices that could replace silicon-based components with organic molecules.
The development of new electronic technologies drives the incessant reduction of functional component sizes. In the context of an international collaborative...
At the LASYS 2018, from June 5th to 7th, the Laser Zentrum Hannover e.V. (LZH) will be showcasing processes for the laser material processing of tomorrow in hall 4 at stand 4E75. With blown bomb shells the LZH will present first results of a research project on civil security.
At this year's LASYS, the LZH will exhibit light-based processes such as cutting, welding, ablation and structuring as well as additive manufacturing for...
There are videos on the internet that can make one marvel at technology. For example, a smartphone is casually bent around the arm or a thin-film display is rolled in all directions and with almost every diameter. From the user's point of view, this looks fantastic. From a professional point of view, however, the question arises: Is that already possible?
At Display Week 2018, scientists from the Fraunhofer Institute for Applied Polymer Research IAP will be demonstrating today’s technological possibilities and...
So-called quantum many-body scars allow quantum systems to stay out of equilibrium much longer, explaining experiment | Study published in Nature Physics
Recently, researchers from Harvard and MIT succeeded in trapping a record 53 atoms and individually controlling their quantum state, realizing what is called a...
02.05.2018 | Event News
13.04.2018 | Event News
12.04.2018 | Event News
25.05.2018 | Life Sciences
25.05.2018 | Interdisciplinary Research
24.05.2018 | Ecology, The Environment and Conservation