Scale-invariant anomaly detection with multiscale group-sparse models
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Image Processing (ICIP) |
| Publisher | IEEE |
| Pages | 3892-3896 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-4673-9961-6 |
| DOIs | |
| Publication status | Published - 19 Aug 2016 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Image Processing - Duration: 1 Jan 1900 → … |
Publication series
| Name | |
|---|---|
| ISSN (Electronic) | 2381-8549 |
Conference
| Conference | IEEE International Conference on Image Processing |
|---|---|
| Period | 1/01/00 → … |
Abstract
The automatic detection of anomalies, defined as patterns that are not encountered in representative set of normal images, is an important problem in industrial control and biomedical applications. We have shown that this problem can be successfully addressed by the sparse representation of individual image patches using a dictionary learned from a large set of patches extracted from normal images. Anomalous patches are detected as those for which the sparse representation on this dictionary exceeds sparsity or error tolerances. Unfortunately, this solution is not suitable for many real-world visual inspection-systems since it is not scale invariant: since the dictionary is learned at a single scale, patches in normal images acquired at a different magnification level might be detected as anomalous. We present an anomaly-detection algorithm that learns a dictionary that is invariant to a range of scale changes, and overcomes this limitation by use of an appropriate sparse coding stage. The algorithm was successfully tested in an industrial application by analyzing a dataset of Scanning Electron Microscope (SEM) images, which typically exhibit different magnification levels.
Keywords
- Detectors, Dictionaries, Encoding, Monitoring, Production, Scanning electron microscopy, Training, Anomaly detection, dictionary learning, group sparsity, image analysis, sparse representations
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Field of science, Statistics Finland
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