JOURNAL ARTICLE

Visual search in virtual environments

Lawrence StarkKoji EzumiTho L. NguyenRazafimandimby Josvah PaulGregory K. TharpHideo Yamashita

Year: 1992 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 1666 Pages: 577-577   Publisher: SPIE

Abstract

A key task in virtual environments is visual search. To obtain quantitative measures of human performance and documentation of visual search strategies, we have used three experimental arrangements--eye, head, and mouse control of viewing windows--by exploiting various combinations of helmet-mounted-displays, graphics workstations, and eye movement tracking facilities. We contrast two different categories of viewing strategies: one, for 2D pictures with large numbers of targets and clutter scattered randomly; the other for quasi-natural 3D scenes with targets and non-targets placed in realistic, sensible positions. Different searching behaviors emerge from these contrasting search conditions, reflecting different visual and perceptual modes. A regular 'searchpattern' is a systematic, repetitive, idiosyncratic sequence of movements carrying the eye to cover the entire 2D scene. Irregular 'searchpatterns' take advantages of wide windows and the wide human visual lobe; here, hierarchical detection and recognition is performed with the appropriate capabilities of the 'two visual systems'. The 'searchpath', also efficient, repetitive and idiosyncratic, provides only a small set of fixations to check continually the smaller number of targets in the naturalistic 3D scene; likely, searchpaths are driven by top-down spatial models. If the viewed object is known and able to be named, then an hypothesized, top-down cognitive model drives active looking in the 'scanpath' mode, again continually checking important subfeatures of the object. Spatial models for searchpaths may be primitive predecessors, in the evolutionary history of animals, of cognitive models for scanpaths.

Keywords:
Computer science Visual search Artificial intelligence Computer vision Contrast (vision) Eye movement Eye tracking Set (abstract data type) Human–computer interaction

Metrics

15
Cited By
1.66
FWCI (Field Weighted Citation Impact)
0
Refs
0.83
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Gaze Tracking and Assistive Technology
Physical Sciences →  Computer Science →  Human-Computer Interaction
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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