Special Issue on Selected Papers from the 17th International FLAIRS Conference (FLAIRS-2004); Guest Editors: Zdravko Markov, Valerie BarrNo Access

MODELING SELECTIVE PERCEPTION OF COMPLEX, NATURAL SCENES

ROXANNE CANOSA

ROXANNE CANOSA

Department of Computer Science, Rochester Institute of Technology, Rochester, New York, USA

Completed as part of dissertation at the Center for Imaging Science, Rochester Institute of Technology Rochester, New York.

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https://doi.org/10.1142/S0218213005002089Cited by:9 (Source: Crossref)

Abstract

Computational modeling of the human visual system is of current interest to developers of artificial vision systems, primarily because a biologically-inspired model can offer solutions to otherwise intractable image understanding problems. The purpose of this study is to present a biologically-inspired model of selective perception that augments a stimulus-driven approach with a high-level algorithm that takes into account particularly informative regions in the scene. The representation is compact and given in the form of a topographic map of relative perceptual conspicuity values. Other recent attempts at compact scene representation consider only low-level information that codes salient features such as color, edge, and luminance values. The previous attempts do not correlate well with subjects' fixation locations during viewing of complex images or natural scenes. This study uses high-level information in the form of figure/ground segmentation, potential object detection, and task-specific location bias. The results correlate well with the fixation densities of human viewers of natural scenes, and can be used as a preprocessing module for image understanding or intelligent surveillance applications.

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