Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision
Abstract Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. This approach aims to detect observations regarded as unusual, erroneous, anomalous, rare, or potentially indicative of fraudulent or mal...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Computer Vision |
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| Online Access: | https://doi.org/10.1049/cvi2.12329 |
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| _version_ | 1850058349863763968 |
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| author | Hakan Cevikalp Robi Polikar Ömer Nezih Gerek Songcan Chen Chuanxing Geng |
| author_facet | Hakan Cevikalp Robi Polikar Ömer Nezih Gerek Songcan Chen Chuanxing Geng |
| author_sort | Hakan Cevikalp |
| collection | DOAJ |
| description | Abstract Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. This approach aims to detect observations regarded as unusual, erroneous, anomalous, rare, or potentially indicative of fraudulent or malicious activity. Open‐set recognition, also referred to as open‐set identification or open‐set classification, is a pattern recognition task that extends traditional classification by addressing the presence of unknown or novel classes during the testing phase. This approach highlights a strong connection between anomaly detection and open‐set recognition, as both seek to identify samples originating from unknown classes or distributions. Open‐set recognition methods frequently involve modelling both known and unknown classes during training, allowing for the capture of the distribution of known classes while explicitly addressing the space of unknown classes. Techniques in open‐set recognition may include outlier detection, density estimation, or configuring decision boundaries to better differentiate between known and unknown classes. This special issue calls for original contributions introducing novel datasets, innovative architectures, and advanced training methods for tasks related to visual anomaly detection and open‐set recognition. |
| format | Article |
| id | doaj-art-8f211fa775bc47f28d0923f92536e8f1 |
| institution | DOAJ |
| issn | 1751-9632 1751-9640 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj-art-8f211fa775bc47f28d0923f92536e8f12025-08-20T02:51:11ZengWileyIET Computer Vision1751-96321751-96402024-12-011881069107110.1049/cvi2.12329Guest Editorial: Anomaly detection and open‐set recognition applications for computer visionHakan Cevikalp0Robi Polikar1Ömer Nezih Gerek2Songcan Chen3Chuanxing Geng4Eskisehir Osmangazi Universitesi Odunpazari TurkeyRowan University Glassboro New Jersey USAEskisehir Technical University Eskisehir TurkeyNanjing University of Aeronautics and Astronautics (NUAA) Nanjing ChinaNanjing University of Aeronautics and Astronautics (NUAA) Nanjing ChinaAbstract Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. This approach aims to detect observations regarded as unusual, erroneous, anomalous, rare, or potentially indicative of fraudulent or malicious activity. Open‐set recognition, also referred to as open‐set identification or open‐set classification, is a pattern recognition task that extends traditional classification by addressing the presence of unknown or novel classes during the testing phase. This approach highlights a strong connection between anomaly detection and open‐set recognition, as both seek to identify samples originating from unknown classes or distributions. Open‐set recognition methods frequently involve modelling both known and unknown classes during training, allowing for the capture of the distribution of known classes while explicitly addressing the space of unknown classes. Techniques in open‐set recognition may include outlier detection, density estimation, or configuring decision boundaries to better differentiate between known and unknown classes. This special issue calls for original contributions introducing novel datasets, innovative architectures, and advanced training methods for tasks related to visual anomaly detection and open‐set recognition.https://doi.org/10.1049/cvi2.12329computer visionobject detection |
| spellingShingle | Hakan Cevikalp Robi Polikar Ömer Nezih Gerek Songcan Chen Chuanxing Geng Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision IET Computer Vision computer vision object detection |
| title | Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision |
| title_full | Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision |
| title_fullStr | Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision |
| title_full_unstemmed | Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision |
| title_short | Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision |
| title_sort | guest editorial anomaly detection and open set recognition applications for computer vision |
| topic | computer vision object detection |
| url | https://doi.org/10.1049/cvi2.12329 |
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