Results 331 to 340 of about 5,378,381 (364)

Detecting motion anomalies

Proceedings of the 8th ACM SIGSPATIAL Workshop on GeoStreaming, 2017
An unsupervised methodology is presented for the detection of motion anomalies using spatial context and multivariate statistical tests. The method is applied to GPS data captured for a taxi fleet in Porto, Portugal; and, AIS data captured for ships operating in the Aegean Sea.
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GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Asian Conference on Computer Vision, 2018
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this
S. Akçay, Amir Atapour-Abarghouei, T. Breckon   +2 more
semanticscholar   +1 more source

Detecting Anomalies and Intruders

2006
Brittleness is a well-known problem in expert systems where a conclusion can be made, which human common sense would recognise as impossible e.g. that a male is pregnant. We have extended previous work on prudent expert systems to enable an expert system to recognise when a case is outside its range of experience.
Akara Prayote, Paul Compton
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Anomaly detection through registration

Pattern Recognition, 1999
Abstract We introduce a system that automatically segments and classifies features in brain MRI volumes. It segments 144 structures of a 256×256×124 voxel image in 18 minutes on an SGI computer with four 194 MHz R10K processors. The algorithm uses an atlas, a hand-segmented and classified MRI of a normal brain, which is warped in 3-D using a ...
Mei Chen, Takeo Kanade, Henry A. Rowley, Dean Pomerleau   +3 more
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Anomalous Anomaly Detection

2022 IEEE International Conference On Artificial Intelligence Testing (AITest), 2022
Muyeed Ahmed, Iulian Neamtiu
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Anomaly detection for internetworms

2005 9th IFIP/IEEE International Symposium on Integrated Network Management, 2005. IM 2005., 2005
Internet worms have become a major threat to the Internet due to their ability to rapidly compromise large numbers of computers. In response to this threat, there is a growing demand for effective techniques to detect the presence of worms and to reduce the worms' spread.
Yousof Al-Hammadi, Christopher Leckie
openaire   +1 more source

Anomaly Detection Based Intrusion Detection

Third International Conference on Information Technology: New Generations (ITNG'06), 2006
This paper is devoted to the problem of neural networks as means of intrusion detection. We show that properly trained neural networks are capable of fast recognition and classification of different attacks. The advantage of the taken approach allows us to demonstrate the superiority of the neural networks over the systems that were created by the ...
Dima Novikov, Roman V. Yampolskiy, Leon Reznik   +2 more
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SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Neural Information Processing Systems
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data.
Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, WU Kai, Y. Liu, Chengjie Wang, Feng Zheng   +7 more
semanticscholar   +1 more source

Anomaly detection in walking trajectory

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Analysis of the walking trajectory and the detection of anomalies in this trajectory, provide important benefits in the fields of health and security. In this work, two methods to detect anomalies in trajectories, are compared. Firstly, an unsupervised method is used where the conformance among trajectories are taken into consideration.
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GT-HAD: Gated Transformer for Hyperspectral Anomaly Detection

IEEE Transactions on Neural Networks and Learning Systems
Hyperspectral anomaly detection (HAD) aims to distinguish between the background and anomalies in a scene, which has been widely adopted in various applications.
Jie Lian, Lizhi Wang, He Sun, Hua Huang
semanticscholar   +1 more source