Paper Info
Title
A Multi-scale Piecewise-Linear Feature Detector for Spectrogram Tracks
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.
Year
DOI
Venue
2009
10.1109/AVSS.2009.84
Genova
Keywords
Field
DocType
spectrogram tracks,multi-scale piecewise-linear feature detector,increasing demand,traffic issue,new framework,traffic data extraction,population evacuation,traffic lights control,feature detector,principal component analysis,pixel,detectors,piecewise linear,spectrogram,parametric,feature detection,time frequency analysis,feature extraction,noise,signal to noise ratio,false positive rate
Computer vision,Pattern recognition,Computer science,Spectrogram,Feature extraction,Parametric statistics,Artificial intelligence,Pixel,Time–frequency analysis,Piecewise linear function,Detector,Principal component analysis
Conference
ISBN
Citations 
PageRank 
978-0-7695-3718-4
2
0.41
References 
Authors
6
3
Name
Order
Citations
PageRank
Thomas A. Lampert1142.70
Nick Pears241030.57
Simon E. M. O'Keefe320.41