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Multistage integration model for human egomotion perception.

G L Zacharias·A X Miao·R Warren
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Abstract

Human computational vision models that attempt to account for the dynamic perception of egomotion and relative depth typically assume a common three-stage process: first, compute the optical flow field based on the dynamically changing image; second, estimate the egomotion states based on the flow; and third, estimate the relative depth/shape based on the egomotion states and possibly on a model of the viewed surface. We propose a model more in line with recent work in human vision, employing multistage integration. Here the dynamic image is first processed to generate spatial and temporal image gradients that drive a mutually interconnected state estimator and depth/shape estimator. The state estimator uses the image gradient information in combination with a depth/shape estimate of the viewed surface and an assumed model of the viewer's dynamics to generate current state estimates; in tandem, the depth/shape estimator uses the image gradient information in combination with the viewer's state estimate and assumed shape model to generate current depth/shape estimates. In this paper, we describe the model and compare model predictions with empirical data.

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AircraftAviationComputer SimulationDepth PerceptionErgonomicsForm PerceptionHumansImage Processing, Computer-AssistedMathematicsModels, PsychologicalMonte Carlo MethodMotion PerceptionNoiseVisual FieldsVisual PerceptionComparative StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

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G L Zacharias, A X Miao, R Warren. Multistage integration model for human egomotion perception.. https://doi.org/10.2514/3.21488

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