Change and Anomaly Detection in Images, Signals and datastreams

**Abstract:**

Change and anomaly detection problems are ubiquitous in science and engineering. The prompt detection of changes or anomalous patterns is often a primary concern, as they provide precious information for understanding the dynamics of a monitored process and for activating suitable countermeasures. Changes, for instance, might indicate an unforeseen evolution of the process generating the data, or a fault in a machinery. Anomalies are typically the most informative regions in an image (e.g., defects in images used for quality control) or the most relevant patterns in a time series (e.g., arrhythmias in ECG tracing) or data stream (e.g. frauds in credit card transactions). Not surprisingly, detection problems in datastreams / time series / images have been widely investigated in the image analysis and pattern recognition communities and are key in application scenarios ranging from quality inspection to health monitoring.

The tutorial presents a rigorous formulation of the change and anomaly-detection problems that fits many signal/image analysis techniques and applications. The tutorial describes in detail the most important approaches in the literature, following the machine-learning perspective of supervised, semi-supervised and unsupervised monitoring tasks. Special emphasis will be given to detection methods based on learned models, which are often adopted to handle images and signals. In particular, these will be divided into traditional models (including autoencoders, learned projections and dictionaries yielding sparse representations) and deep learning models (including CNNs, deep-one-class classifiers and deep generative models)

The tutorial is accompanied by various examples where change/anomaly detection algorithms are applied to solve real world problems. These include health applications (ECG monitoring in wearable devices), quality control (image analysis solutions to detect defects and anomalous patterns in industrial manufacturing), and fraud detection in a datastream of credit card transactions.

Tutorial 2: Change and Anomaly Detection in Images, Signals and datastreams // HALF DAY

tutorial

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Giacomo Boracchi

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Change and Anomaly Detection in Images, Signals and datastreams

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