Abstract—We present a novel network to deconvolve mixtures of inputs that have been previously learned, but more importantly, to segment the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that deconvolution is determined by the amplitude of an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs.
By: A. Ravishankar Rao; Guillermo A. Cecchi; Charles C. Peck; James R. Kozloski
Published in: RC23838 in 2005
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.
Questions about this service can be mailed to reports@us.ibm.com .