Abstract
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 −1dB 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.
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