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Related Concept Videos

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Related Experiment Video

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Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Image interpretation by a single bottom-up top-down cycle.

Boris Epshtein1, Ita Lifshitz, Shimon Ullman

  • 1Department of Computer Science, The Weizmann Institute of Science, Rehovot 76100, Israel.

Proceedings of the National Academy of Sciences of the United States of America
|September 18, 2008
PubMed
Summary
This summary is machine-generated.

The human visual system rapidly recognizes objects and parts. A new model shows simultaneous object and part recognition using feed-forward and feedback processing in the visual cortex.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Computer Vision

Background:

  • Standard models of visual object recognition rely on feed-forward processing.
  • This approach struggles as object parts are often ambiguous without whole object context.
  • Prior detection and localization of the entire object are typically required.

Purpose of the Study:

  • To propose a new model for object recognition that overcomes limitations of standard feed-forward models.
  • To explain how the visual cortex achieves rapid and accurate recognition of both objects and their parts.
  • To demonstrate a mechanism for simultaneous recognition and localization at multiple hierarchical levels.

Main Methods:

  • Development of a cortical-like hierarchical model.
  • Simulation of visual processing through feed-forward and feedback sweeps.
  • Analysis of recognition and localization performance at various levels of the hierarchy.

Main Results:

  • The proposed model achieves recognition and localization of objects and parts at multiple levels.
  • This occurs nearly simultaneously within a single processing cycle.
  • The process involves a feed-forward sweep followed by a feedback sweep.

Conclusions:

  • A hierarchical model incorporating both feed-forward and feedback pathways can explain rapid and accurate visual recognition.
  • This model provides a more comprehensive understanding of how the human visual system processes complex visual information.
  • Simultaneous recognition of objects and parts is achievable through integrated hierarchical processing.