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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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AI approaches to computer vision, Feature detection and feature extraction, Object recognition.
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DISCIPLINE:
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Computer Science
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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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103-397-076
(Last edited on
2003/11/21 20:59:20 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 machine-learned image interpretation systems have shown expert-level 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, design-cycle time, and
robustness, such systems are still severely limited. This paper reviews the anatomy of the state-of-theart Multi resolution Adaptive Object Recognition framework (MR ADORE) and presents extensions that aim at removing the last vestiges of human intervention still present in the original design of ADORE. More specifically, feature selection is still a task performed by human domain experts thereby prohibiting automatic creation of image interpretation systems. This paper focuses on autonomous feature extraction methods aimed at removing the need for
human expertise in the feature selection process.
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