Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An approach to detect lofar lines
Pattern Recognition Letters
An introduction to genetic algorithms
An introduction to genetic algorithms
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-scale Piecewise-Linear Feature Detector for Spectrogram Tracks
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Incremental Laplacian eigenmaps by preserving adjacent information between data points
Pattern Recognition Letters
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
Masking of time-frequency patterns in applications of passive underwater target detection
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
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
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
An approach to extract straight lines with subpixel accuracy
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Neurocomputing
On the detection of tracks in spectrogram images
Pattern Recognition
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Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation-it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com.