کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
532230 | 869923 | 2013 | 13 صفحه PDF | دانلود رایگان |
This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as −1 dB in the frequency domain. These theoretical and experimental findings are related to existing solutions to the problem, offering a new insight into their limitations. We show, through complexity analysis, that this is achievable in real-time.
► Commonly employed continuity and curvature measures are not suitable, and increase false positive detection rates.
► A curvature measure based on geometric properties of the track accurately models various track appearances.
► Track detection methods based upon individual pixels are unreliable at low SNRs.
► The proposed active contour algorithm effectively detects tracks of varying appearances.
► The line location accuracy metric is flawed and can rate good and bad detections similarly.
Journal: Pattern Recognition - Volume 46, Issue 5, May 2013, Pages 1396–1408