Tutorials

Sensor fusion techniques are widely used in many real-world data science applications such as autonomous systems, remote sensing, video surveillance and military. The objective of using data fusion in multisensor environments is to combine the data provided by the multiple sensors to obtain a lower detection error probability and a higher reliability. The data can be obtained from the same sensor with several capturing parameters or multiple sensors. This tutorial presents the recent advances in deep learning sensor fusion specifically for three main computer vision tasks: classification, detection and segmentation. We will illustrate this taxonomy through relevant examples from the literature and will highlight existing open challenges and research directions that might inspire attendees to embark in the fascinating and promising area of deep learning-based sensor fusion methods.

Brain-machine Interface (BMI) which utilizes brain signals to realize the subject intention is the promising solution for disabilities. The widely used Electroencephalogram (EEG) requires extensive preprocessing to extract meaningful features. Then a classifier generates the recognition results based on the extracted features. This kind of system can be further utilized to control in real time the robot.