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16 September 1992 Neural network model for human visual perception of 3-D curvilinear motion
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Abstract
This paper presents a neural network model to emulate the ability of the human visual system to detect changes in heading direction, i.e. curvilinear motion. The network consists of three layers. The input to the network is a two-dimensional velocity field, and the output is a signal representing the magnitude and the direction of the rotational component in the flow. The first layer of the network computes local differences vectors of the velocity field to define the orientation of the translational field lines. The second layer of the network extracts the instantaneous heading direction from the translational component of the velocity field. And the third layer determines the rotational component of the velocity field. The magnitude of perceived curvilinear motion is directly proportional to the magnitude of the rotational component. The simulation results match psychophysical data of four human subjects at both slow (2.0 m/s) and fast (26.4 m/s) locomotion speeds. The biological feasibility of this neural network is supported by finding in biological vision systems.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoming Wang and Kathleen Turano "Neural network model for human visual perception of 3-D curvilinear motion", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138294
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