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Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR SystemJPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.
Document ID
20150005807
Acquisition Source
Jet Propulsion Laboratory
Document Type
Other
External Source(s)
Authors
Lin, Tsung Han (Hank)
(California Univ., San Diego La Jolla, CA, United States)
Date Acquired
April 17, 2015
Publication Date
August 1, 2011
Subject Category
Computer Programming And Software
Distribution Limits
Public
Copyright
Other
Keywords
Adaboost
false alarm rate
Texton filters
automatic target recognition
correlation
Gentleboost

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