In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects. 

In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects. 

In this weekly meeting the CVG members come together and discuss recent topics in the Computer Vision and Machine Learning community. In addition there are typically two presentations of selected papers or student projects. 

Our newest member of the group, Luigi Fiorillo, will introduce his current research titled InteractiveDeepSleepNet: An interactive automatic sleep scoring system using raw single-channel EEG in our next lab meeting. Luigi joins our lab as an external PhD student co-supervised by Prof. Paolo Favaro and Prof. Dr. Francesca Faraci. Please find the abstract of his talk below.

Abstract

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. An automatic sleep scoring results in a simple classification problems which aims to predict, for each 30-seconds epoch of polysomnography, the correct sleep stage label. It could appear then a suitable task for artificial intelligence algorithms. Indeed, machine learning (ML) algorithms have been applied to sleep scoring for many years. As a result, several software products offer nowadays automated or semi-automated scoring services. However, the vast majority of the sleep physicians do not use them. Very recently, thanks to the increased computational power, deep learning (DL) has also been employed with promising results. ML and DL algorithms can undoubtedly reach a high accuracy in specific situations, but there are many difficulties in their introduction in the daily routine.  We believe that the reason why ML and DL scoring system continues not to be integrated in the hospital routine is because the knowledge of the physician fails to be integrated in the scoring process. A user-centric approach including physicians in the learning phase, directly interacting with the algorithm, could be successful in sleep scoring. Combination of a deep learning architecture, using raw single-channel EEG data with a human-centered approach has the potential of leading to a well accepted software.

Abstract

Until now, the task of  stitching multiple overlapping images to a bigger, panoramic picture is solely approached with "classical", hardcoded algorithms while deep learning is at most used for speci c subtasks. This talk introduces a novel end-to-end neural network approach to image stitching called StitchNet, which uses a (pretrained) autoencoder and deep convolutional networks. Additionally to presenting several new datasets for the task of supervised image stitching with each 120'000 training and 5'000 validation samples, this talk also presents various experiments with different kinds of existing networks designed for image superresolution and image segmentation adapted to the task of image stitching.