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- W2101330735 abstract "JPL 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." @default.
- W2101330735 created "2016-06-24" @default.
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- W2101330735 date "2011-08-01" @default.
- W2101330735 modified "2023-09-26" @default.
- W2101330735 title "Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System" @default.
- W2101330735 hasPublicationYear "2011" @default.
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