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Symbolic dynamic filtering for image analysis: theory and experimental validation
2009
Signal, Image and Video Processing
Recent literature has reported the theory of symbolic dynamic filtering (SDF) of one-dimensional timeseries data and its various applications for anomaly detection and pattern recognition. This paper extends the theory of SDF in the two-dimensional domain, where symbol sequences are generated from image data (i.e., pixels). Given the symbol sequence, a probabilistic finite state automaton (PFSA), called the D-Markov machine, is constructed on the principles of Markov random fields to
doi:10.1007/s11760-009-0122-7
fatcat:ax7blmjd7fbflo6ipr6jvox6um