Q. Q. Huynh

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| Underwater mammal sound classiication is demonstrated using a novel application of wavelet time/frequency decomposition and feature extraction using a BCM unsupervised network. Diierent feature extraction methods and diierent wavelet representations are studied. The system achieves outstanding classiication performance even when tested with mammal sounds(More)
We investigate various image enhancement techniques geared towards a speciic detector. Our database consists of side-scan sonar images collected at the Naval Surface Warfare Center (NSWC), and the detector we use has proven to have excellent results on these data. We start by investigating various wavelet and wavelet packet denoising methods. Other methods(More)
We introduce a method of enhancing a sonar image with acoustic color. Our method emphasizes the discriminative properties of the returned spectrum for the purpose of object discrimination. This is achieved by analyzing the discriminating power of different frequency bands and associating the appropriate ones with a color map. INTRODUCTION Current underwater(More)
— We integrate several key components of a pattern recognition system for a mine-like targets detection problem. These include several image enhancements, post-processing and multi-expert fusion. The image enhancement includes wavelet de-noising and classical computer vision methods such as nonlinear and adaptive equalization and other filters. Our approach(More)
Good discrimination results have been obtained with an active backscatter data set of mine-like objects [1], where the task was to distinguish between man-made and non-man-made objects. In this work we introduce a novel method for constructing best basis for discrimination from wavelet packets, and demonstrate the superiority of multiple ensembles of(More)
—Underwater mammal sound classification is demonstrated using a novel application of wavelet time–frequency decomposition and feature extraction using a Bienenstock, Cooper, and Munro (BCM) unsupervised network. Different feature extraction methods and different wavelet representations are studied. The system achieves outstanding classification performance(More)
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