An automatic approach to the detection and extraction of mine features in sidescan sonar

Reed, Scott; Pétillot, Yvan; Bell, Judith

doi:10.1109/joe.2002.808199

Cited by 195 publications

(118 citation statements)

References 27 publications

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“…* Military: Identification of land mines from shallow sub-surface radar data [108] and identification of underwater mines from sonar data [65] can lead to automatic detection.…”

Section: Applicationsmentioning

confidence: 99%

Three-dimensional object registration using wavelet features

Chalfant

Patrikalakis

2009

Engineering with Computers

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Recent developments in shape-based modeling and data acquisition have brought three-dimensional models to the forefront of computer graphics and visualization research. New data acquisition methods are producing large numbers of models in a variety of fields. Three-dimensional shape-based matching and registration (alignment) are key to the useful application of such models in areas from automated surface inspection to cancer detection and surgery. The three-dimensional models in these applications are typically huge. State-of-the-art simulations in computational fluid dynamics produce upward of four terabytes of data per second of flow. Research-level magnetic resonance imaging (MRI) resolutions can reach 1 cubic micro-meter. As a result, object registration and matching algorithms must handle very large amounts of data.The algorithms developed in this thesis accomplish automatic registration and matching of three-dimensional voxelized models. We employ features in a wavelet transform domain to accomplish registration. The features are extracted in a multiresolutional format, thus delineating features at various scales for robust and rapid matching. Registration is achieved through seeking peaks in sets of rotation quaternions using a voting scheme, then separately identifying translation. The method is robust to occlusion, clutter and noise. The efficacy of the algorithm is demonstrated through examples from solid modeling and medical imaging applications.

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“…* Military: Identification of land mines from shallow sub-surface radar data [108] and identification of underwater mines from sonar data [65] can lead to automatic detection.…”

Section: Applicationsmentioning

confidence: 99%

Three-dimensional object registration using wavelet features

Chalfant

Patrikalakis

2009

Engineering with Computers

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“…A sea-mine appears in the sonar images as a bar shaped object-highlight accompanied by a ARTICLE IN PRESS shadow which represents the hiding of the seabottom-reverberation by the sea-mine [19]. Mignotte and Collet [18] presented 3-class Markovian segmentation method for the detection of sea-mines in sonar images.…”

Section: Sea-mine Sonar Imagesmentioning

confidence: 99%

“…12. Examples of sea-mine sonar images: sea-mines appear in the sonar images as a bar shaped object-highlight accompanied by a shadow which represents the hiding of seabottom-reverberation by the sea-mine [19].…”

Section: Sea-mine Sonar Imagesmentioning

confidence: 99%

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Anomaly subspace detection based on a multi-scale Markov random field model

Goldman

Cohen

2005

Signal Processing

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In this paper we introduce a multi-scale Gaussian Markov random field (GMRF) model and a corresponding anomaly subspace detection algorithm. Natural clutter images, often appear to have several periodical patterns of various period lengths. In such cases, the GMRF model may not sufficiently describe the clutter image. The proposed model is based on a multi-scale wavelet representation of the image, independent component analysis, and modeling each independent component as a GMRF. Anomaly detection is subsequently carried out by applying a matched subspace detector to the innovations process generated by the presumed model. The robustness of the proposed approach is demonstrated with application to automatic target detection in synthetic and real imagery. A quantitative performance analysis and experimental results demonstrate the advantage of the proposed method in comparison to competing methods. r

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“…Unsupervised approaches have also been attempted. Amongst the most promising is the work of Reed et al [16]. They used unsupervised Markov random field based detection to segment the image into shadow, seabed, and object highlight regions.…”

Section: Introductionmentioning

confidence: 99%

Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

Nelson

Kingsbury

2012

IET Signal Process.

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An anomaly detection approach is considered for the mine hunting in sonar imagery problem. We exploit previous work that used dual-tree wavelets and fractal dimension to adaptively suppress sand ripples and a matched filter as an initial detector. Here, lacunarity inspired features are extracted from the remaining false positives, again using dual-tree wavelets. A one-class support vector machine is then used to learn a decision boundary, based only on these false positives. The approach exploits the large quantities of 'normal' natural background data available but avoids the difficult requirement of collecting examples of targets in order to train a classifier.

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An automatic approach to the detection and extraction of mine features in sidescan sonar

Cited by 195 publications

References 27 publications

Three-dimensional object registration using wavelet features

Three-dimensional object registration using wavelet features

Anomaly subspace detection based on a multi-scale Markov random field model

Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

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