Target confirmation architecture for a buried object scanning sonar

Sternlicht,; Dikeman,; Lemonds,; Korporaal,; Teranishi,

doi:10.1109/oceans.2003.178631

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Under recent concept of operations, automatics underwater vehicles (AUVs) based systems hosting low-frequency sonars are first used to search-classify-map relatively large areas at a high search rate. Abundant sonar systems were developed in the last two decades, such as side-scan sonar [2], volumetric sonar [3], and EO [4]. SCM is fast but does not exactly determine the mines.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Visualization System for Deep-Sea Surveying

Zhang

et al. 2014

Mathematical Problems in Engineering

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Remote robotic exploration holds vast potential for gaining knowledge about extreme environments, which is difficult to be accessed by humans. In the last two decades, various underwater devices were developed for detecting the mines and mine-like objects in the deep-sea environment. However, there are some problems in recent equipment, like poor accuracy of mineral objects detection, without real-time processing, and low resolution of underwater video frames. Consequently, the underwater objects recognition is a difficult task, because the physical properties of the medium, the captured video frames, are distorted seriously. In this paper, we are considering use of the modern image processing methods to determine the mineral location and to recognize the mineral actually within a little computation complex. We firstly analyze the recent underwater imaging models and propose a novel underwater optical imaging model, which is much closer to the light propagation model in the underwater environment. In our imaging system, we remove the electrical noise by dual-tree complex wavelet transform. And then we solve the nonuniform illumination of artificial lights by fast guided trilateral bilateral filter and recover the image color through automatic color equalization. Finally, a shape-based mineral recognition algorithm is proposed for underwater objects detection. These methods are designed for real-time execution on limited-memory platforms. This pipeline is suitable for detecting underwater objects in practice by our experiences. The initial results are presented and experiments demonstrate the effectiveness of the proposed real-time visualization system.

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Section: Introductionmentioning

confidence: 99%

Real-Time Visualization System for Deep-Sea Surveying

Zhang

et al. 2014

Mathematical Problems in Engineering

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“…For buried targets, we employ low-frequency simulations and a detection/classification method based on [13]. First, thresholding is used to discard all pixels below a chosen brightness, to remove low-intensity background clutter.…”

Section: B Low-frequency Feature Extractionmentioning

confidence: 99%

“…For both frequency categories, a joint Gaussian-Bayesian target classifier [12,13] is implemented that measures feature distributions, transforms the original feature distributions into Gaussian distributions, computes feature covariance to exploit feature correlation, and classifies the detected object using a log-likelihood ratio (LLR) test. The LLR is computed:…”

Section: Classificationmentioning

confidence: 99%

Acoustic Modeling for Sea-Mine CAD/CAC Development

Harbaugh

Sternlicht

Putney

et al.

Proceedings of OCEANS 2005 MTS/IEEE

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Computer Aided Detection/Classification (CAD/ CAC) algorithms have been developed to help mine warfare operators correctly identify targets within vast quantities of Synthetic Aperture Sonar/Side Look Sonar (SAS/SLS) imagery. Typically, large, and thus expensive, sets of target and clutter exemplars are required for the evaluation and training of these algorithms. To expedite the assessment and preliminary training of CAD/CAC algorithms for systems under development, physicsbased acoustic models can be utilized to generate synthetic datasets. Model-based recognition algorithms that predict exemplars for a given search hypothesis may use acoustic models to reduce dependence on exemplar databases. Acoustic models predicting the scatter from proud and buried targets are developed and described in this paper. A rigid-target model, using boundary integral equations for the target and a sediment-scatter model, is applied for proud and partially buried targets. This model is suitable for high-frequency, narrowband sonars. An elastic-target model using a spheroidal T matrix approach is employed for buried targets. This model captures the responses appropriate for systems that scan the sediment volume. The synthetic, acoustic datasets produced are processed into SAS images and subsequently used to investigate candidate feature sets and for CAD/ CAC algorithm development.

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Multi-frequency Backscatter Variations for Classification of Mine-like Targets from Low-resolution Sonar Data

Stack

Dobeck

Bernstein

Proceedings of OCEANS 2005 MTS/IEEE

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Target confirmation architecture for a buried object scanning sonar

Cited by 5 publications

References 9 publications

Real-Time Visualization System for Deep-Sea Surveying

Real-Time Visualization System for Deep-Sea Surveying

Acoustic Modeling for Sea-Mine CAD/CAC Development

Multi-frequency Backscatter Variations for Classification of Mine-like Targets from Low-resolution Sonar Data

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