Reducing anomaly detection in images to detection in noise
Resumen:
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail. Hence, the general and impossible background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our approach is therefore unsupervised and works on arbitrary images.
| 2018 | |
|
Anomaly detection Saliency Self-similarity Procesamiento de Señales |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/43544 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522942409506816 |
|---|---|
| author | Davy, Axel |
| author2 | Ehret, Thibaud Morel, Jean-Michel Delbracio, Mauricio |
| author2_role | author author author |
| author_facet | Davy, Axel Ehret, Thibaud Morel, Jean-Michel Delbracio, Mauricio |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Davy, Axel Ehret, Thibaud Morel, Jean-Michel Delbracio, Mauricio |
| dc.date.accessioned.none.fl_str_mv | 2024-04-16T16:21:19Z |
| dc.date.available.none.fl_str_mv | 2024-04-16T16:21:19Z |
| dc.date.issued.es.fl_str_mv | 2018 |
| dc.date.submitted.es.fl_str_mv | 20240416 |
| dc.description.abstract.none.fl_txt_mv | Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail. Hence, the general and impossible background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our approach is therefore unsupervised and works on arbitrary images. |
| dc.description.es.fl_txt_mv | Trabajo presentado al 25th IEEE International Conference on Image Processing (ICIP) |
| dc.identifier.citation.es.fl_str_mv | Davy, A, Ehret, T, Morel, J.M, Delbracio, M. "Reducing anomaly detection in images to detection in noise" Publicado en: Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Atenas, Grecia, 07-10 oct., 2018, pp. 1058-1062, doi: 10.1109/ICIP.2018.8451059. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/43544 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Anomaly detection Saliency Self-similarity |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Reducing anomaly detection in images to detection in noise |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo presentado al 25th IEEE International Conference on Image Processing (ICIP) |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_e72c73e049f62898b59ae3aec71e05bb |
| identifier_str_mv | Davy, A, Ehret, T, Morel, J.M, Delbracio, M. "Reducing anomaly detection in images to detection in noise" Publicado en: Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Atenas, Grecia, 07-10 oct., 2018, pp. 1058-1062, doi: 10.1109/ICIP.2018.8451059. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/43544 |
| publishDate | 2018 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2024-04-16T16:21:19Z2024-04-16T16:21:19Z201820240416Davy, A, Ehret, T, Morel, J.M, Delbracio, M. "Reducing anomaly detection in images to detection in noise" Publicado en: Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Atenas, Grecia, 07-10 oct., 2018, pp. 1058-1062, doi: 10.1109/ICIP.2018.8451059.https://hdl.handle.net/20.500.12008/43544Trabajo presentado al 25th IEEE International Conference on Image Processing (ICIP)Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail. Hence, the general and impossible background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our approach is therefore unsupervised and works on arbitrary images.Made available in DSpace on 2024-04-16T16:21:19Z (GMT). No. of bitstreams: 5 DEMD18.pdf: 2928706 bytes, checksum: 6af577247b8e2af11591ae1fd6fbd8ac (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2018enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Anomaly detectionSaliencySelf-similarityProcesamiento de SeñalesReducing anomaly detection in images to detection in noisePonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaDavy, AxelEhret, ThibaudMorel, Jean-MichelDelbracio, MauricioProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Reducing anomaly detection in images to detection in noise Davy, Axel Anomaly detection Saliency Self-similarity Procesamiento de Señales |
| status_str | publishedVersion |
| title | Reducing anomaly detection in images to detection in noise |
| title_full | Reducing anomaly detection in images to detection in noise |
| title_fullStr | Reducing anomaly detection in images to detection in noise |
| title_full_unstemmed | Reducing anomaly detection in images to detection in noise |
| title_short | Reducing anomaly detection in images to detection in noise |
| title_sort | Reducing anomaly detection in images to detection in noise |
| topic | Anomaly detection Saliency Self-similarity Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/43544 |
