Attentional Selection for Object Recognition — A Gentle Way
Abstract
Attentional selection of an object for recognition is often modeled using all-or-nothing switching of neuronal connection pathway from the attended region of the retinal input to the recognition units. However, there is little physiological evidence for such all-or-none modulation in early areas. We present a combined model for spatial attention and object recognition in which the recognition system monitors the entire visual field, but attentional modulation by as little as 20% at a high level is sufficient to recognize multiple objects. To determine the size and shape of the region to be modulated, a rough segmentation is performed, based on pre-attentive features already computed to guide attention. Testing with synthetic and natural stimuli demonstrates that our new approach to attentional selection for recognition yields encouraging results in addition to being biologically plausible.
Additional Information
© Springer-Verlag Berlin Heidelberg 2002. This work was supported by NSF (ITR, ERC and KDI), NEI, NIMA, DARPA, the Zumberge Faculty Research and Innovation Fund (L.I.), the Charles Lee Powell Foundation, a McDonnell-Pew Award in Cognitive Neuroscience (M.R.), Eastman Kodak Company, Honda R&D Co., Ltd., ITRI, Komatsu Ltd., Siemens Corporate Research, Inc., Toyota Motor Corporation and The Whitaker Foundation.Attached Files
Accepted Version - 445.pdf
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Additional details
- Eprint ID
- 40539
- DOI
- 10.1007/3-540-36181-2_47
- Resolver ID
- CaltechAUTHORS:20130816-103235815
- NSF
- NEI
- NIMA
- Defense Advanced Research Projects Agency (DARPA)
- Zumberge Faculty Research and Innovation Fund
- Charles Lee Powell Foundation
- McDonnell-Pew Award in Cognitive Neuroscience
- Eastman Kodak Company
- Honda R&D Co.
- ITRI
- Komatsu Ltd.
- Siemens Corporate Research, Inc.
- Toyota Motor Corporation
- Whitaker Foundation
- Created
-
2008-01-11Created from EPrint's datestamp field
- Updated
-
2021-11-09Created from EPrint's last_modified field
- Caltech groups
- Koch Laboratory (KLAB)
- Series Name
- Lecture Notes in Computer Science
- Series Volume or Issue Number
- 2525