Publications

Diplegia is one of the most common forms of a broad family of motion disorders named cerebral palsy (CP) affecting the voluntary muscular system. In recent years, various classification criteria have been proposed for CP, to assist in diagnosis, clinical decision-making and communication. In this manuscript, we divide the spastic forms of CP into 4 other categories according to a previous classification criterion and propose a machine learning approach for automatically classifying patients. Training and validation of our approach are based on data about 200 patients acquired using 19 markers and high frequency VICON cameras in an Italian hospital. Our approach makes use of the latest deep learning techniques. More specifically, it involves a multi-layer perceptron network (MLP), combined with Fourier analysis. An encouraging classification performance is obtained for two of the four classes.

Estimating the focus of attention of a person looking at an image or a video is a crucial step which can enhance many vision-based inference mechanisms: image segmentation and annotation, video captioning, autonomous driving are some examples. The early stages of the attentive behavior are typically bottom-up; reproducing the same mechanism means to find the saliency embodied in the images, i.e. which parts of an image pop out of a visual scene. This process has been studied for decades in neuroscience and in terms of computational models for reproducing the human cortical process. In the last few years, early models have been replaced by deep learning architectures, that outperform any early approach compared against public datasets. In this paper, we propose a discussion on why convolutional neural networks (CNNs) are so accurate in saliency prediction. We present our DL architectures which combine both bottom-up cues and higher-level semantics, and incorporate the concept of time in the attentional process through LSTM recurrent architectures. Eventually, we present a video-specific architecture based on the C3D network, which can extracts spatio-temporal features by means of 3D convolutions to model task-driven attentive behaviors. The merit of this work is to show how these deep networks are not mere brute-force methods tuned on massive amount of data, but represent well-defined architectures which recall very closely the early saliency models, although improved with the semantics learned by human ground-thuth.

We present a novel video browsing and retrieval system for edited videos, in which videos are automatically decomposed into meaningful and storytelling parts (i.e. scenes) and tagged according to their transcript. The system relies on a Triplet Deep Neural Network which exploits multimodal features, and has been implemented as a set of extensions to the eXo Platform Enterprise Content Management System (ECMS). This set of extensions enable the interactive visualization of a video, its automatic and semi-automatic annotation, as well as a keyword-based search inside the video collection. The platform also allows a natural integration with third-party add-ons, so that automatic annotations can be exploited outside the proposed platform.

We present AMHUSE (A Multimodal dataset for HUmour SEnsing) along with a novel web-based annotation tool named DANTE (Di- mensional ANnotation Tool for Emotions). The dataset is the result of an experiment concerning amusement elicitation, involving 36 subjects in order to record the reactions in presence of 3 amusing and 1 neutral video stimuli. Gathered data include RGB video and depth sequences along with physiological responses (electrodermal activity, blood volume pulse, temperature). The videos were later annotated by 4 experts in terms of valence and arousal continuous dimensions. Both the dataset and the annotation tool are made publicly available for research purposes.

In this paper, we propose a novel scene detection algorithm which employs semantic, visual, textual and audio cues. We also show how the hierarchical decomposition of the storytelling video structure can improve retrieval results presentation with semantically and aesthetically effective thumbnails. Our method is built upon two advancements of the state of the art: 1) semantic feature extraction which builds video specific concept detectors; 2) multimodal feature embedding learning, that maps the feature vector of a shot to a space in which the Euclidean distance has task specific semantic properties. The proposed method is able to decompose the video in annotated temporal segments which allow for a query specific thumbnail extraction. Extensive experiments are performed on different data sets to demonstrate the effectiveness of our algorithm. An in-depth discussion on how to deal with the subjectivity of the task is conducted and a strategy to overcome the problem is suggested.

We discuss modelling issues related to the design of a somatic facial motor space. The variants proposed are conceived to be part of a larger system for dealing with simulation-based face emotion analysis along dual interactions.

The current Cultural Heritage management system lacks of ICT platforms for the management and integration of heterogeneous and fragmented data sources and interconnection between private and public subjects involved in the process. The SACHER project intends to fill this gap, working both on a technological level and on a business model level: firstly providing a platform based on an open-source distributed cloud-computing environment for the management of the complete data lifecycle related to cultural assets; moreover providing new models based on participatory design for Cultural Heritage data directed towards social entrepreneurship. This paper presents the first implementation of a system for managing data based on the 3D model of the cultural object, with a focus on the process for cultural assets management and the interface design for cultural services.