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CONTRIBUTORS:
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CONFERENCE TITLE:
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CONF. LOCATION:
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None
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YEAR:
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2003
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PUB TYPE:
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Conference Paper
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SUBJECT(S):
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Adaptive and Machine Learning, Intelligent Image Processing and Computer Vision.
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DISCIPLINE:
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Cultural Studies
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HTTP:
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LANGUAGE:
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English
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PUB ID:
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103-397-075
(Last edited on
2003/11/21 20:59:01 US/Mountain)
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SPONSOR(S):
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ABSTRACT:
Automated image interpretation is an important task in numerous applications ranging from security systems to natural resource inventorization based on remote-sensing.
Recently, a second generation of adaptive machinelearned
image interpretation systems have shown expertlevel performance in several challenging domains. While demonstrating an unprecedented improvement over hand-engineered and first generation machine-learned systems in
terms of cross-domain portability, and design-cycle time,
such systems have yet to be rigorously tested. This paper
inspects the anatomy of the state-of-the-art Multi-Resolution Adaptive Object Recognition framework (MR ADORE)
and presents experimental results aimed at establishing the
robustness of the system to real-world image perturbations.
Tested in a challenging domain of forestry, MR ADORE is
shown to be robust to changes in sun angle, camera angle
and training signal accuracy.
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