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Mobility and low contrast trip hazard avoidance using augmented depth

delete2014-11-26
delete36
PRE
AI
C
Chris McCarthy *
J
Janine Walker
P
Paulette Lieby
A
Adele F. Scott
N
Nick Barnes
DOI:10.1088/1741-2560/12/1/016003delete
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Abstract

Abstract

En 中文
Objective. We evaluated a novel visual representation for current and near-term prosthetic vision. Augmented depth emphasizes ground obstacles and floor-wall boundaries in a depth-based visual representation. This is achieved by artificially increasing contrast between obstacles and the ground surface via a novel ground plane extraction algorithm specifically designed to preserve low-contrast ground-surface boundaries. Approach. The effectiveness of augmented depth was examined in human mobility trials compared against standard intensity-based (Intensity), depth-based (Depth) and random (Random) visual representations. Eight participants with normal vision used simulated prosthetic vision with 20 phosphenes and eight perceivable brightness levels to traverse a course with randomly placed small and low-contrast obstacles on the ground. Main results. The number of collisions was significantly reduced using augmented depth, compared with intensity, depth and random representations (48%, 44% and 72% less collisions, respectively). Significance. These results indicate that augmented depth may enable safe mobility in the presence of low-contrast obstacles with current and near-term implants. This is the first demonstration that an augmentation of the scene ensuring key objects are visible may provide better outcomes for prosthetic vision.
Keywords:
prosthetic vision
mobility
computer vision
navigation
obstacle avoidance
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Journal

Journal of Neural Engineering cover
Journal of Neural Engineering
IF:
3.8
Papers:
4.0K
Citations:
1.4W

Organization

A
Australian National University
Scholars:
2.1W
Papers: 2.3W
Citations: 3.9W