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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.

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Related Experiment Video

Updated: May 25, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
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Ground surface segmentation for navigation with a low resolution visual prosthesis.

Chris McCarthy1, Nick Barnes, Paulette Lieby

  • 1NICTA Canberra Research Laboratory, Canberra ACT, Australia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

Ground surface segmentation improves obstacle detection in low-resolution prosthetic vision. This technique enhances the distinction between the ground and hazards, aiding visual navigation for prosthetic users.

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Area of Science:

  • Computer Vision
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Prosthetic visual representations often lack detail, making obstacle perception challenging.
  • Low to medium resolution visual data can obscure critical environmental information for navigation.
  • Distinguishing the ground surface from obstacles is crucial for safe mobility.

Purpose of the Study:

  • To enhance obstacle perception in low to medium resolution prosthetic visual representations.
  • To improve the distinction between traversable ground surfaces and obstructions.
  • To evaluate the utility of ground surface segmentation for visual prostheses.

Main Methods:

  • Applied a stereo disparity data segmentation algorithm to identify traversable space.
  • Utilized ground surface segmentation to differentiate the ground from obstacles, including trip hazards.
  • Conducted qualitative comparisons with intensity and depth-based representations.

Main Results:

  • Ground surface segmentation effectively enhanced the visualization of obstacles.
  • The proposed method improved the distinction between the ground and potential trip hazards.
  • Qualitative comparisons showed advantages over traditional intensity and depth representations.

Conclusions:

  • Ground surface segmentation offers significant potential for improving obstacle visualization in prosthetic vision.
  • This technique can enhance visual navigation capabilities for users of low resolution and low dynamic range visual prostheses.
  • Further development could lead to safer and more intuitive prosthetic visual feedback systems.