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Related Concept Videos

Depth Perception and Spatial Vision01:15

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.
Vision01:24

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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.
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.
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Anatomy of the Eyeball01:20

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...

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Related Experiment Video

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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
07:45

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Published on: July 21, 2020

Binocular robot vision emulating disparity computation in the primary visual cortex.

Kazuhiro Shimonomura1, Takayuki Kushima, Tetsuya Yagi

  • 1The Center for Advanced Medical Engineering and Informatics, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|February 15, 2008
PubMed
Summary
This summary is machine-generated.

This study presents a novel VLSI binocular vision system that mimics the primary visual cortex (V1) for real-time disparity computation. The hardware system efficiently processes visual information for enhanced depth perception capabilities.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Biomimetic systems

Background:

  • The primary visual cortex (V1) performs complex disparity computations for depth perception.
  • Existing computational models often require significant processing power and are not hardware-efficient.
  • Emulating biological visual processing in hardware offers potential for real-time applications.

Purpose of the Study:

  • To design and implement a Very Large Scale Integration (VLSI) binocular vision system.
  • To emulate the disparity computation mechanisms found in the primary visual cortex (V1).
  • To achieve real-time disparity mapping using a compact, hardware-based approach.

Main Methods:

  • Developed a hierarchical system comprising silicon retinas, orientation chips, and a Field Programmable Gate Array (FPGA).
  • Silicon retinas emulate retinal receptive fields (Laplacian-Gaussian-like).
  • Orientation chips generate orientation-selective receptive fields (Gabor-like) mimicking simple cells.
  • FPGA implements the disparity energy model to compute complex cell responses and disparity maps.

Main Results:

  • The system successfully emulates the disparity computation of the primary visual cortex (V1).
  • It provides responses of complex cells tuned to five different disparities.
  • A disparity map is generated by comparing energy outputs from the system.
  • Real-time disparity computation is achieved through a combination of analog and digital hardware.

Conclusions:

  • The designed VLSI binocular vision system effectively mimics V1 disparity computation.
  • The hardware architecture enables compact and real-time processing of visual depth information.
  • This approach holds promise for advanced robotic vision and other real-time visual processing applications.