Name
Abstract | ||
|---|---|---|
Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD’s limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00138-020-01059-4 | Machine Vision and Applications |
Keywords | Field | DocType |
Anomaly detection, Graph Fourier transform, Graph-based image processing, Principal component analysis, Hyperspectral images, PET | Laplacian matrix,Anomaly detection,Inversion (meteorology),Computer science,Algorithm,Hyperspectral imaging,Multivariate normal distribution,Covariance matrix,Spatial contextual awareness,Detector | Journal |
Volume | Issue | ISSN |
31 | 1 | 0932-8092 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Verdoja | 1 | 5 | 4.79 |
| Marco Grangetto | 2 | 456 | 42.27 |