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

TY - GEN

T1 - A Multi-Scale Piecewise-Linear Feature Detector for Spectrogram Tracks

AU - Lampert, Thomas

AU - Pears, Nick

AU - O'Keefe, Simon

N1 - © 2009 IEEE. This is an author produced version of the published paper. Uploaded in accordance with the publisher's self-archiving policy.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - feature extraction

KW - pattern classification

KW - piecewise linear techniques

KW - equal error rates

KW - image content

KW - multiscale piecewise-linear feature detector

KW - spectrogram track classification

KW - spectrogram track detection

KW - spectrogram

UR - http://www.scopus.com/inward/record.url?scp=72449164150&partnerID=8YFLogxK

U2 - 10.1109/AVSS.2009.84

DO - 10.1109/AVSS.2009.84

M3 - Conference contribution

SN - 978-1-4244-4755-8

SP - 330

EP - 335

BT - AVSS: 2009 6TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE

PB - IEEE

CY - NEW YORK

T2 - 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009

Y2 - 2 September 2009 through 4 September 2009

ER -