Wavelet methods for time series and spatial data

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Real time anomaly detection and categorisation

The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. The resultant procedure is capable of real-time analysis and categorisation between baseline and two forms of anomalous structure: point and collective anomalies. Various theoretical properties of the procedure are derived. These, together with an extensive simulation study, highlight that the average run length to false alarm and the average detection delay of the proposed online algorithm are very close to that of the offline version. Experiments on simulated and real data are provided to demonstrate the benefits of the proposed method

A test of stationarity for textured images

This article proposes a test of stationarity for random fields on a regular lattice motivated by a problem arising from texture analysis. Our approach is founded on the locally stationary two-dimensional wavelet (LS2W) process model for lattice processes which has previously been used for standard texture analysis tasks such as texture discrimination and classification. We propose two variants of our stationarity test, both of which can be performed on a single realisation – a feature of particular practical importance within texture analysis. We illustrate our approach with pilled fabric data, demonstrating that the test is capable of identifying visually-subtle changes in stationarity. Supplementary material for this article is available online

Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series

Distributed acoustic sensing technology is increasingly being used to support production and well management within the oil and gas sector, for example to improve flow monitoring and production profiling. This sensing technology is capable of recording substantial data volumes at multiple depths within an oil well, giving unprecedented insights into production behaviour. However the technology is also prone to recording periods of anomalous behaviour, where the same physical features are concurrently observed at multiple depths. Such features are called ‘stripes’ and are undesirable, detrimentally affecting well performance modelling. This paper focuses on the important challenge of developing a principled approach to identifying such anomalous periods within distributed acoustic signals. We extend recent work on classifying locally stationary wavelet time series to an online setting and, in so doing, introduce a computationally-efficient online procedure capable of accurately identifying anomalous regions within multivariate time series. © 2019, The Author(s)

Similarity Based Cross-Section Segmentation in Rough Log End Images

Part 14: Image Video Processing 4International audienceThis work treats cross-section (CS) segmentation in digital images of rough wood log ends. Existing CS segmentation approaches are focused on computed tomography CS images of logs and no approach and experimental evaluation for digital images has been presented so far. Segmentation of cross-sections in rough log end images is a prerequisite for the development of novel log end analysis applications (e.g. biometric log recognition or automated log grading). We propose a simple and fast computable similarity-based region growing algorithm for CS segmentation. In our experiments we evaluate different texture features (Local binary patterns & Intensity histograms) and histogram distances. Results show that the algorithm achieves the most accurate results in combination with intensity histograms and the earth movers distance. Generally, we conclude that for certain applications simple texture features and a matured distance metric can outperform higher-order texture features and basic distance metrics