Lampert, Thomas, O'Keefe, Simon orcid.org/0000-0001-5957-2474 and Pears, Nick orcid.org/0000-0001-9513-5634 (2009) Line Detection Methods for Spectrogram Images. In: Kurzynski, Marek and Wozniak, Michal, (eds.) Computer Recognition Systems. 6th International Conference on Computer Recognition Systems, 25-28 May 2009 Advances in Intelligent and Soft Computing . Springer , POL , pp. 127-134.
Abstract
Accurate feature detection is key to higher level decisions regarding image content. Within the domain of spectrogram track detection and classification, the detection problem is compounded by low signal to noise ratios and high track appearance variation. Evaluation of standard feature detection methods present in the literature is essential to determine their strengths and weaknesses in this domain. With this knowledge, improved detection strategies can be developed. This paper presents a comparison of line detectors and a novel linear feature detector able to detect tracks of varying gradients. It is shown that the Equal Error Rates of existing methods are high, highlighting the need for research into novel detectors. Preliminary results obtained with a limited implementation of the novel method are presented which demonstrate an improvement over those evaluated.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2009 Springer. This is an author produced version of the published paper. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 Jun 2012 18:10 |
Last Modified: | 17 Dec 2024 00:32 |
Published Version: | https://doi.org/10.1007/978-3-540-93905-4_16 |
Status: | Published |
Publisher: | Springer |
Series Name: | Advances in Intelligent and Soft Computing |
Identification Number: | 10.1007/978-3-540-93905-4_16 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:67983 |