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dc.contributor.authorGrossbergy, Stephenen_US
dc.contributor.authorWyse, Lonceen_US
dc.date.accessioned2011-11-14T18:21:47Z
dc.date.available2011-11-14T18:21:47Z
dc.date.issued1991-05en_US
dc.identifier.urihttp://hdl.handle.net/2144/2067
dc.description.abstractA neural network model, called an FBF network, is proposed for automatic parallel separation of multiple image figures from each other and their backgrounds in noisy grayscale or multi-colored images. The figures can then be processed in parallel by an array of self-organizing Adaptive Resonance Theory (ART) neural networks for automatic target recognition. An FBF network can automatically separate the disconnected but interleaved spirals that Minsky and Papert introduced in their book Perceptrons. The network's design also clarifies why humans cannot rapidly separate interleaved spirals, yet can rapidly detect conjunctions of disparity and color, or of disparity and motion, that distinguish target figures from surrounding distractors. Figure-ground separation is accomplished by iterating operations of a Feature Contour System (FCS) and a Boundary Contour System (BCS) in the order FCS-BCS-FCS, hence the term FBF, that have been derived from an analysis of biological vision. The FCS operations include the use of nonlinear shunting networks to compensate for variable illumination and nonlinear diffusion networks to control filling-in. A key new feature of an FBF network is the use of filling-in for figure-ground separation. The BCS operations include oriented filters joined to competitive and cooperative interactions designed to detect, regularize, and complete boundaries in up to 50 percent noise, while suppressing the noise. A modified CORT-X filter is described which uses both on-cells and off-cells to generate a boundary segmentation from a noisy image.en_US
dc.description.sponsorshipAir Force Office of Scientific Research (90-0175); Army Research Office (DAAL-03-88-K0088); Defense Advanced Research Projects Agency (90-0083); Hughes Research Laboratories (S1-804481-D, S1-903136); American Society for Engineering Educationen_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1991-012en_US
dc.rightsCopyright 1991 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectVision
dc.subjectSensor fusion
dc.subjectFigure-ground separation
dc.subjectSegmentation
dc.subjectNeural networks
dc.subjectPattern recognition
dc.subjectFilling-in
dc.subjectVisual cortex
dc.titleA Neural Network Architecture for Figure-ground Separation of Connected Scenic Figuresen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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