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Proceedings Paper

Hierarchical scene understanding exploiting automatically derived contextual data
Author(s): Kenneth Sullivan; Shivkumar Chandrasekaran; Kaushal Solanki; B. S. Manjunath; Jayanth Nayak; Luca Bertelli
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Paper Abstract

In this paper we present methods for scene understanding, localization and classification of complex, visually heterogeneous objects from overhead imagery. Key features of this work include: determining boundaries of objects within large field-of-view images, classification of increasingly complex object classes through hierarchical descriptions, and exploiting automatically extracted hypotheses about the surrounding region to improve classification of a more localized region. Our system uses a principled probabilistic approach to classify increasingly larger and more complex regions, and then iteratively uses this automatically determined contextual information to reduce false alarms and misclassifications.

Paper Details

Date Published: 27 April 2010
PDF: 12 pages
Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 769717 (27 April 2010); doi: 10.1117/12.850665
Show Author Affiliations
Kenneth Sullivan, Mayachitra, Inc. (United States)
Shivkumar Chandrasekaran, Mayachitra, Inc. (United States)
Kaushal Solanki, Mayachitra, Inc. (United States)
B. S. Manjunath, Mayachitra, Inc. (United States)
Jayanth Nayak, Mayachitra, Inc. (United States)
Luca Bertelli, Mayachitra, Inc. (United States)

Published in SPIE Proceedings Vol. 7697:
Signal Processing, Sensor Fusion, and Target Recognition XIX
Ivan Kadar, Editor(s)

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