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Proceedings Paper

ALISA: adaptive learning image and signal analysis
Author(s): Peter Bock
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Paper Abstract

ALISA (Adaptive Learning Image and Signal Analysis) is an adaptive statistical learning engine that may be used to detect and classify the surfaces and boundaries of objects in images. The engine has been designed, implemented, and tested at both the George Washington University and the Research Institute for Applied Knowledge Processing in Ulm, Germany over the last nine years with major funding from Robert Bosch GmbH and Lockheed-Martin Corporation. The design of ALISA was inspired by the multi-path cortical- column architecture and adaptive functions of the mammalian visual cortex.

Paper Details

Date Published: 29 January 1999
PDF: 14 pages
Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); doi: 10.1117/12.339821
Show Author Affiliations
Peter Bock, George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 3584:
27th AIPR Workshop: Advances in Computer-Assisted Recognition
Robert J. Mericsko, Editor(s)

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