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    • CAS/CNS Technical Reports
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    •   OpenBU
    • College of Arts and Sciences
    • Cognitive & Neural Systems
    • CAS/CNS Technical Reports
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    Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

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    04.008.pdf (1.140Mb)
    Date Issued
    2004-10
    Author
    Carpenter, Gail
    Martens, Siegfried
    Mingolla, Ennio
    Ogas, Ogi
    Sai, Chaitanya
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    Permanent Link
    https://hdl.handle.net/2144/1928
    Abstract
    Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.
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    Copyright 2004 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.
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    • CAS/CNS Technical Reports [485]

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