By the same authors

A Multi-Scale Piecewise-Linear Feature Detector for Spectrogram Tracks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Full text download(s)

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

Title of host publicationAVSS: 2009 6TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE
DatePublished - 2009
Pages330-335
Number of pages6
PublisherIEEE
Place of PublicationNEW YORK
Original languageEnglish
ISBN (Print)978-1-4244-4755-8

Abstract

Reliable feature detection is a prerequisite to higher level decisions regarding image content. In the domain of spectrogram track detection and classification, the detection problem is compounded by low signal-to-noise ratios and high variation in track appearance. Evaluation of standard feature detection methods 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, multi-scale, linear feature detector able to detect tracks of varying gradients. We outline improvements to the multi-scale search strategies which reduce run-time costs. It is shown that the Equal Error Rates of existing methods are high, highlighting the need for research into novel detectors. Results demonstrate that the proposed method offers an improvement in detection rates when compared to other, state of the art, methods whilst keeping false positive rates low. It is also shown that a multi-scale implementation offers an improvement over fixed scale implementations.

Bibliographical note

© 2009 IEEE. This is an author produced version of the published paper. Uploaded in accordance with the publisher's self-archiving policy.

    Research areas

  • feature extraction, pattern classification , piecewise linear techniques , equal error rates , image content , multiscale piecewise-linear feature detector , spectrogram track classification, spectrogram track detection , spectrogram

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations