Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Statistical Image Sequence Processing for Temporal Change Detection
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Change detection in sequences of images by multifractal analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Extensions of vector quantization for incremental clustering
Pattern Recognition
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A new monitoring design for uni-variate statistical quality control charts
Information Sciences: an International Journal
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
Learning in Non-Stationary Environments: Methods and Applications
Learning in Non-Stationary Environments: Methods and Applications
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Incremental Learning Based on Growing Gaussian Mixture Models
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 02
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Surveillance, safety and security of evolving systems are a challenge to prevent accident. The dynamic detection of a hypothetical and theoretical blockage incident in the Phenix nuclear reactor is investigated. Such an incident is characterized by abnormal temperature rises in the neighbourhood of the concerned reactor core assembly. The dataset is the output temperature map of the reactor, it is provided by the Atomic Energy and Alternative Energies Commission (CEA). A real time approach is proposed, based on a sliding temporal window, it is divided into two steps. The first one behaves like a sieve, its function is to detect simultaneous temperature evolutions in a close neighbourhood which may induce a potential incident. When such evolutions are detected, the second step computes the temperature contrast between each assembly having these evolutions and its neighbourhood. This method permits to monitor the system evolution in real time while only few observations are required. Results are validated on various noisy realistic simulated perturbations.