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Vision

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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.

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Related Experiment Video

Updated: Jul 7, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

A general framework for low level vision.

N Sochen¹, R Kimmel, R Malladi

¹Dept. of Phys., California Univ., Berkeley, CA 94720, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 16, 2008

Summary

This summary is machine-generated.

We present a novel geometric framework for image processing, treating images as surfaces in a higher-dimensional space. This approach unifies existing methods and enables efficient image enhancement and denoising algorithms.

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Area of Science:

Computer Vision
Image Processing
Differential Geometry

Background:

Classical image processing methods often lack a unified theoretical foundation.
Existing scale-space and enhancement techniques can be computationally intensive.
Representing images geometrically offers potential for novel algorithms.

Purpose of the Study:

To introduce a new geometrical framework for image scale-space and enhancement.
To unify and extend classical image processing schemes.
To develop efficient and powerful algorithms for image denoising and scale-space representation.

Main Methods:

Representing intensity images as surfaces in (x, I) space.
Formulating image scale-space and enhancement as geometrical flows.
Extending the framework to multidimensional signals, including color images.

Main Results:

A unified geometrical framework for image scale-space and enhancement.
New and efficient schemes derived from the geometrical formulation.
Demonstration of powerful denoising and scale-space algorithms for multidimensional signals.

Conclusions:

The proposed geometrical framework provides a unified and powerful approach to image processing.
The method naturally extends to complex image types and multidimensional signals.
This framework leads to efficient and effective algorithms for image enhancement and denoising.