Novelty detection: a review—part 1: statistical approaches
Signal Processing
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Neural Network-Based Novelty Detector for Image Sequence Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncertainty principles, extractors, and explicit embeddings of l2 into l1
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Expert Systems with Applications: An International Journal
Detecting the most unusual part of two- and three-dimensional digital images
Pattern Recognition
Detecting the most unusual part of a digital image
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
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In this paper we show that by using a modification of our previously developed probabilistic method for finding the most unusual part of a 3D digital image, we can detect the temporal intervals and areas of interest in the signals/video and mark the corresponding objects that behave in an unusual way. Due to the different dynamics along the temporal and the spatial axes, namely the prevalence of the cylinder-like objects in the video and the pseudo-periodic slowly changing spectral characteristics of the bio-electrical signals, an additional step is needed to treat the temporal axis. One of the possible practical applications of the method can be in Intensive Care hospital Units (ICU), where EEG video recording is a standard practice to ensure that a potentially life-threatening event can be detected even if its indications are present only in a fraction of the observed signals.