Underwater buried object recognition using wavelet packets and Fourier descriptors

Boulinguez, D.; Quinquis, André

doi:10.1109/iciap.1999.797641

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…However, few methods are specifically designed for use with ultrasonic signals. In [4] , a relative threshold level representing a fraction of the absolute value of the largest coefficient is used to filter an acoustic data collected from an acquisition system based on parametric sonar. Other types of thresholds such as absolute quantitative threshold, relative quantitative threshold, absolute threshold, the thresholds VisuShrink [8] and SureShrink [10] of Donoho and Johnston as well as many other complex thresholding methods are proposed in literature.…”

Section: Resultsmentioning

confidence: 99%

“…Underwater objects identification is of a great importance to a large number of environmental problems and civilian applications [1][2][3][4][5][6] , such as seabed mapping, archaeological object recognition, and wreckage detection. The availability and the expensive cost of ultrasonic tests in real mediums such as sea and lakes has led to the use of laboratory tanks for measurements, with gated signals and time-windowing to isolate reflections.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Compressing Ultrasonic Data Algorithm Based on Wavelets

Hergli¹,

Baili²,

Bouslama³

et al. 2005

American J. of Applied Sciences

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Abstract:In this paper, we propose a novel and effective algorithm for compressing large volumes of data recorded in underwater applications, using a laboratory designed experimental setup. This data represents samples of ultrasonic signals acquired at different geometric positions of a water tank. The proposed algorithm was based on Wavelets and consists of two processing phases including a prefiltering stage using wavelets to eliminate non significant values thus reducing the number of nonzero coefficients and a second processing stage to eliminate the redundancies present in the signal.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Compressing Ultrasonic Data Algorithm Based on Wavelets

Hergli¹,

Baili²,

Bouslama³

et al. 2005

American J. of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The source angle remains the same, but there is now a receiver angle that is not in the same plane as the receiver in general. With these considerations, the linear system model of the received signal then becomes (2) where the superscripts and correspond to the receiver and the source, respectively.…”

Section: A Signal Characterizationmentioning

confidence: 99%

“…Both of these results can be regarded as forms of data diversity, which may be used as an aid in cases where traditional high-frequency monostatic imaging fails. In sonar applications, there has been relatively little study of alternative techniques for buried target classification, although some laboratory-based studies have shown promise [2]- [4]. Data diversity techniques have been effectively employed in the buried landmine case [5], [6], where, as in the underwater case, the imaging wavelength is on the order of the target size.…”

Section: Introductionmentioning

confidence: 99%

Bistatic synthetic aperture target detection and imaging with an AUV

Edwards

Schmidt

LePage

2001

IEEE J. Oceanic Eng.

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The acoustic detection and classification of completely and partially buried objects in the multipath environment of the coastal ocean presents a major challenge to the mine countermeasures (MCM) community. However, the rapidly emerging autonomous underwater vehicle (AUV) technology provides the opportunity of exploring entirely new sonar concepts based on mono-, bi-or multi-static configurations. For example, the medium frequency regime (1-10 kHz) with its bottom penetration advantage may be explored using large synthetic apertures, where acoustic information is accumulated over a series of sonar pings. The performance of such approaches is highly dependent on accurate platform navigation and timing, which poses a significant challenge to AUV developers, particularly because the navigation procedures are themselves dependent on the complicated multipath acoustic environment. Data from the GOATS'98 experiment have been analyzed to investigate the feasibility of combining seabed scattering data from consecutive pings of a fixed parametric source to form a bistatic synthetic aperture for target localization and imaging with an AUV based receiving platform. The paper describes different levels of bistatic processing including both incoherent and coherent beamforming and very large aperture interferometric approaches, and the associated performance tradeoffs are discussed.

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“…Using the shape based techniques; human faces are recognized based on Fourier descriptors using Neural Networks [3]. Underwater objects are described based on wavelet packets and Fourier descriptors [4].However, Fourier descriptor fails in determining shape information of object's corner. Though several shape signatures have been formed [5] for Fourier description, still ideal is yet to be described.…”

Section: Introductionmentioning

confidence: 99%

A Simple Region Descriptor Based on Object Area per Scan Line

Kumar¹,

Ramareddy²,

Rao³

2010

IJCA

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In the field of image processing, identifying object is based on appropriately chosen descriptors. The proper choice of descriptors in pattern recognition is the most sensitive criteria as small misjudgments may lead to wrong identification. There have been several algorithms proposed and worked in this field. Here, the idea is to identify the objects in an uncomplicated method while being computationally efficient. This paper is based on identifying the patterns of objects / polygons based on the recording area of the objects per line scan. The descriptors here are invariant to translation and become invariant to scaling after normalization. Here the objects considered are regular polygons in various background conditions. In order to reduce the noise, after segmentation by thresholding along with labeling and area filtering is done. Along with polygon identification, descriptors for all the numbers are also shown. In order to identify the objects, the average magnitude difference function (AMDF) is applied to each characteristic curve. This paper also shows that though AMDF is a dissimilarity measure, it works better here than auto correlation function (ACF), which is a similarity measure.

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Underwater buried object recognition using wavelet packets and Fourier descriptors

Cited by 5 publications

References 7 publications

A New Compressing Ultrasonic Data Algorithm Based on Wavelets

A New Compressing Ultrasonic Data Algorithm Based on Wavelets

Bistatic synthetic aperture target detection and imaging with an AUV

A Simple Region Descriptor Based on Object Area per Scan Line

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