NDLI: Temporal information as top-down context in binocular disparity detection

Content Provider	IEEE Xplore Digital Library
Author	Solgi, M. Juyang Weng
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824, USA (Solgi, M.; Juyang Weng)
Abstract	Recently, it has been shown that motor initiated context through top-down connections boosts the performance of network models in object recognition applications. Moreover, models of the 6-layer architecture of the laminar cortex have been shown to have computational advantage over single-layer models of the cortex. In this study, we present a temporal model of the laminar cortex that applies expectation feedback signals as top-down temporal context in a binocular network supervised to learn disparities. The work reported here shows that the 6-layer architecture drastically reduced the disparity detection error by as much as 7 times with context enabled. Top-down context reduced the error by a factor of 2 in the same 6-layer architecture. For the first time, an end-to-end model inspired by the 6-layer architecture with emergent binocular representation has reached a sub-pixel accuracy in the challenging problem of binocular disparity detection from natural images. In addition, our model demonstrates biologically-plausible gradually changing topographic maps; the representation of disparity sensitivity changes smoothly along the cortex.
Starting Page	1
Ending Page	7
File Size	1014003
Page Count	7
File Format	PDF
ISBN	9781424441174
DOI	10.1109/DEVLRN.2009.5175533
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Biological system modeling Computational modeling Circuits Computer architecture Object detection Brain modeling Object recognition Pixel Context modeling
Content Type	Text
Resource Type	Article

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