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Vision

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.

Color Vision

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

What is a Sensory System?

What is a Sensory System?

Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.

Predator-Prey Interactions

Predator-Prey Interactions

Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.

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Related Experiment Video

Updated: Feb 8, 2026

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

Colour and illumination in computer vision.

Graham D Finlayson¹

¹School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.

Interface Focus

|June 29, 2018

Summary

This summary is machine-generated.

Illumination estimation in computer vision is often complex. This study proposes a simpler, homography-based bias correction method that achieves leading performance with significantly lower computational cost than deep learning approaches.

Keywords:

colour illumination learning

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Area of Science:

Computer Vision
Image Processing

Background:

Illumination estimation is crucial for tasks like color-based image recognition and tracking.
Traditional methods analyze image statistics, often requiring bias correction.
Deep learning offers high complexity but may not be the optimal solution.

Purpose of the Study:

To challenge the dominance of deep learning in illuminant estimation.
To introduce a novel, efficient approach to bias correction in illumination estimation algorithms.

Main Methods:

Review of historical and contemporary illumination estimation algorithms.
Focus on the bias correction stage of classical algorithms.
Development of an exposure-invariant bias correction method framed as solving for a homography.

Main Results:

The proposed homography-based method demonstrates leading illumination estimation performance.
Achieves state-of-the-art results with a fraction of the complexity of deep learning models.
Highlights the effectiveness of exposure-invariant bias correction.

Conclusions:

Illuminant estimation can be effectively achieved through simpler, homography-based bias correction.
This approach offers a more computationally efficient alternative to complex deep learning methods.
Challenges the assumption that deep learning is always the superior method for illuminant estimation.