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A neural net based architecture for the segmentation of mixed gray-level and binary picturesA neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.
Document ID
19910044560
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Tabatabai, Ali
(Bell Communications Research, Inc. Red Bank, NJ, United States)
Troudet, Terry P.
(NASA Lewis Research Center; Sverdrup Technology, Inc. Cleveland, OH, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1991
Publication Information
Publication: IEEE Transactions on Circuits and Systems
Volume: 38
ISSN: 0098-4094
Subject Category
Cybernetics
Accession Number
91A29183
Distribution Limits
Public
Copyright
Other

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