On active contour models and balloons
CVGIP: Image Understanding
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
An approach to detect lofar lines
Pattern Recognition Letters
Fast Multiple-Precision Evaluation of Elementary Functions
Journal of the ACM (JACM)
Introduction to algorithms
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Snakes for Medical Images Segmentation
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
Locating object contours in complex background using improved snakes
Computer Vision and Image Understanding
An active contour algorithm for spectrogram track detection
Pattern Recognition Letters
Lofargram line tracking by multistage decision process
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
A detailed investigation into low-level feature detection in spectrogram images
Pattern Recognition
Multiple target tracking and multiple frequency line tracking usinghidden Markov models
IEEE Transactions on Signal Processing
Estimating frequency by interpolation using Fourier coefficients
IEEE Transactions on Signal Processing
Single tone parameter estimation from discrete-time observations
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
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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.