An approach to detect lofar lines

Martino, Joseph Di; Tabbone, Salvatore

doi:10.1016/0167-8655(95)00088-7

Cited by 15 publications

(15 citation statements)

References 8 publications

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“…Research into the use of automatic detection methods increased with the advent of reliable computational algorithms during the 1980s, 1990s and early 21st century. The research area has attracted contributions from a variety of backgrounds, ranging from statistical modelling [41], image processing [1,10] and expert systems [35]. The problem can be compounded, not only by a low signal-to-noise ratio (SNR) in a spectrogram, which is the result of weak periodic phenomena embedded within noisy time-series data, but also by the variability of a track's structure with time.…”

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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“…Allowing the SNR of S, in terms of decibels, to be calculated (4). Which forms the y-axis of our plot in Fig.…”

Section: Spectrogram Complexity Measure (Scm)mentioning

confidence: 99%

“…It follows from this that, if a source which emits narrowband energy is present during some consecutive time frames a track, or line, will be present within the spectrogram. The problem of detecting these tracks is an ongoing area of research with contributions from a variety of backgrounds ranging from statistical modelling [1] to image processing [2][3][4]. This research area forms a critical stage in the detection and classification of sources in passive sonar systems and the analysis of vibration data.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Framework for Spectrogram Track Detection Algorithms

Lampert

O’Keefe

2009

Advances in Intelligent and Soft Computing

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Abstract. This paper presents a method to facilitate performance comparisons between different spectrogram track detection algorithms tested upon different data sets. There is no standard test database which is carefully tailored to test the different aspects of such algorithms and real data is often proprietary in nature. This naturally hinders the ability to perform comparisons between a developing algorithm and those which exist in the literature using the same test database. The method presented in this paper will allow researchers to present, in a graphical way, information regarding the data on which they test their algorithm while not disclosing proprietary information.

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“…It follows from this that, if a source which emits narrowband energy is present during some consecutive time frames then a track, which is often linear, will be present within the spectrogram. The problem of detecting these tracks is an ongoing area of research with contributions from a variety of backgrounds ranging from statistical modelling [13] and image processing [7,1,9] to expert systems [8]. This problem is a critical stage in the detection and classification of sources in passive sonar systems and the analysis of vibration data.…”

Section: Introductionmentioning

confidence: 99%