1 November 1991 Neural network model of dynamic form perception: implications of retinal persistence and extraretinal sharpening for the perception of moving boundaries
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Abstract
While temporal properties of the visual system have been the subject of extensive research in psychology, many computational theories are based on steady-state behavior. For example, Marr's theory requires early measurements to be instantaneous. Furthermore, optimizational type approaches to perception are designed around properties of equilibria, and very little attention is devoted to the relevance of trajectories to perceptual experience. Electrophysiological findings however show that visual neurons such as retinal ganglion cells possess strong transient components. Therefore, a fundamental issue in perceptual sciences is the understanding of the relevance of these transient components to visual perception. This study claims that adaptive, nonmonotonic transient properties of early visual units are crucial components in visual processing. An extra-retinal feedback on-center off-surround anatomy is proposed to sharpen the 'blurred output' from the retinal level. Based on theoretical studies of pattern transformation properties of recurrent networks for sustained inputs we propose a global model (including retina and extra-retinal areas) of visual processing where a reset from transient ganglion cells of the retina prevent smearing for moving images. The model provides a theoretical link between hyperacuity (achieved by denser extra-retinal packing and nonlinear contrast enhancement) and visual masking (resulting from inter-layer and intra-channel inhibition mechanisms).
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haluk Ogmen, Haluk Ogmen, } "Neural network model of dynamic form perception: implications of retinal persistence and extraretinal sharpening for the perception of moving boundaries", Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50326; https://doi.org/10.1117/12.50326
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