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Vision01:24

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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Visual System01:26

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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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Prosopagnosia01:24

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Anatomy of the Eyeball01:20

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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...
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Related Experiment Video

Updated: Dec 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Semantic and structural image segmentation for prosthetic vision.

Melani Sanchez-Garcia1, Ruben Martinez-Cantin1, Jose J Guerrero1

  • 1Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain.

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Summary
This summary is machine-generated.

Computer vision enhances prosthetic vision by creating schematic representations of indoor environments. This approach improves object recognition and room identification for visually impaired individuals using simulated phosphene images.

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Neuroscience

Background:

  • Prosthetic vision aims to restore sight but faces limitations in image bandwidth, resolution, and contrast.
  • Current prosthetic vision systems offer restricted object recognition and scene understanding for users.
  • Computer vision offers a potential solution to enhance visual information processing in prosthetic vision.

Purpose of the Study:

  • To develop a novel computer vision approach for generating schematic representations of indoor environments.
  • To optimize visual information for simulated phosphene images used in prosthetic vision.
  • To improve object recognition and scene understanding for users of visual prosthetics.

Main Methods:

  • Utilized a combination of convolutional neural networks (CNNs).
  • Extracted structural informative edges and segmented object silhouettes from indoor scenes.
  • Developed a method for creating schematic representations tailored for simulated phosphene images.

Main Results:

  • The proposed method demonstrated high accuracy in object recognition tasks.
  • Accurate room identification was achieved for indoor scenes using the developed approach.
  • Experimental results with simulated prosthetic vision showed significant improvements compared to existing methods.

Conclusions:

  • The developed computer vision approach effectively enhances prosthetic vision by providing a schematic environmental representation.
  • This method improves the limited information bandwidth of phosphene images, aiding users in object and scene recognition.
  • The findings suggest a promising direction for improving the functional capabilities of visual prosthetics.