Computational Model Reveals How The Brain Recognizes Objects

MIT News

How the brain recognizes objects

A new computational model sheds light on the workings of the human visual system and could help advance artificial-intelligence research, too.

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 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.

What and where: A Bayesian inference theory of visual attention


In the theoretical framework described in this thesis, attention is part of the inference process that solves the visual recognition problem of what is where. The theory proposes a computational role for attention and leads to a model that predicts some of its main properties at the level of psychophysics and physiology. [...]

Attention and Object recognition in Videos


Collected from: Home (sharatsc)

How the brain recognizes objects

Computational model sheds light on how the brain recognizes objects

Home (sharatsc)

YouTube - Recognizing objects in video

A neuromoprhic approach to computer vision

New model reveals how the brain identifies objects

How the brain recognizes objects

ScienceDirect - Vision Research : What and where: A Bayesian inference theory of attention

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Tomaso Poggio

Thomas Serre: Research

Thesis Defense: Sharat Chikkerur