Lofargram line tracking by multistage decision process

Martino, Joseph Di; Haton, Jean Paul; Laporte, A.

doi:10.1109/icassp.1993.319119

Cited by 26 publications

(24 citation statements)

References 5 publications

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“…It is found that a number of mechanisms are in use, however, there exists no systematic investigation into the advantages and disadvantages of each. Abel et al [1], Di Martino et al [9], Scharf and Elliot [46] and Paris and Jauffret [41], to name but a few, take the approach of detecting singlepixel instances of the tracks, therefore only intensity information can be exploited in the decision process. Methods such as those presented by Gillespie [17], Kendall et al [25] and Leeming [34] use windows in a spectrogram to train neural network classifiers-the benefits of this, however, were not investigated and the research was probably motivated for the ability to use neural networks.…”

Section: Introductionmentioning

confidence: 99%

A detailed investigation into low-level feature detection in spectrogram images

Lampert

O’Keefe

2011

Pattern Recognition

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Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation-it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com.

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Section: Introductionmentioning

confidence: 99%

A detailed investigation into low-level feature detection in spectrogram images

Lampert

O’Keefe

2011

Pattern Recognition

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“…In frequency domain, Martino performed LOFAR (Low Frequency Analysis and Recording) broadband analysis, estimates the noise vibration of the target machinery [10]. Borowski and et al in [11] investigated analysis under water signal in frequency domain.…”

Section: Appl Sci 2018 8 61mentioning

confidence: 99%

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

Alaie

Farsi

2018

Applied Sciences

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This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold) that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS) adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method.

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“…Di Martino et al define perceptual track features derived from feature grouping theory [13]. These are: frequential curvature regularity; temporal continuity; high average intensity; and high point density.…”

Section: Related Workmentioning

confidence: 99%

On the detection of tracks in spectrogram images

Lampert

O’Keefe

2013

Pattern Recognition

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This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as −1 dB in the frequency domain. These theoretical and experimental findings are related to existing solutions to the problem, offering a new insight into their limitations. We show, through complexity analysis, that this is achievable in real-time.

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Lofargram line tracking by multistage decision process

Cited by 26 publications

References 5 publications

A detailed investigation into low-level feature detection in spectrogram images

A detailed investigation into low-level feature detection in spectrogram images

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

On the detection of tracks in spectrogram images

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