@conference {Cenci:2017:MAP:3109761.3109773, title = {Movements Analysis of Preterm Infants by Using Depth Sensor}, booktitle = {Proceedings of the 1st International Conference on Internet of Things and Machine Learning}, series = {IML {\textquoteright}17}, year = {2017}, month = {10-2017}, pages = {12:1{\textendash}12:9}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Qualitative assessment of general movements in preterm infants is widely used in clinical practice. It can enable early detection of neurological dysfunctions and consequent neuromotor impairments in high risk infants. However, the outcome of these assessments is not standardized and it is influenced by examiner{\textquoteright}s subjective interpretation. For this reason, there is an increasing interest in the use of automated movement recognition technologies being applied in this field. In this work, we use a video-based system for preterm infant{\textquoteright}s movements assessment to provide a 3D\ motion analysis method able to extract some important indicators from the sequence of depth images collected by using an RGB-D sensor placed over the infant lying on the crib. The advantage of the proposed method is that it is objective, contactless, non-invasive, easy to install, affordable and suitable to be used in an indoor environment with poor lighting, as might be rooms in the Neonatal Intensive Care Unit, where these infants are taken into care. Experimental results show that the proposed method is able to derive from statistical analysis of depth data some key performance indicators, each of which describes different characteristics of the infant{\textquoteright}s spontaneous movements. Preliminary tests are conducted in the experimental phase on a preterm infant hospitalized in a women{\textquoteright}s and children{\textquoteright}s hospital. The project can be used to investigate the relationship between the characteristics of spontaneous movements and the presence of pathologies as cerebral palsy or other minor neurological dysfunctions.

}, keywords = {3D tracking, clustering, modelling, preterm infant{\textquoteright}s movement analysis}, isbn = {978-1-4503-5243-7}, doi = {10.1145/3109761.3109773}, url = {http://doi.acm.org/10.1145/3109761.3109773}, author = {Annalisa Cenci and Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Virgilio Paolo Carnielli} } @conference {mancini2016multi, title = {A multi/hyper-spectral imaging system for land use/land cover using unmanned aerial systems}, booktitle = {Unmanned Aircraft Systems (ICUAS), 2016 International Conference on}, year = {2016}, pages = {1148{\textendash}1155}, publisher = {IEEE}, organization = {IEEE}, author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti} } @article {Fulvio2016273, title = {Multi-Point Stereovision System for Contactless Dimensional Measurements}, journal = {Journal of Intelligent and Robotic Systems: Theory and Applications}, volume = {81}, number = {2}, year = {2016}, note = {cited By 0}, pages = {273-284}, abstract = {

The paper focuses on the design of a fast and reliable multi-point vision-based measurement system able to be flexible, low cost and accurate for quality control in industrial robotics applications. The paper discusses a new method for integrating visual quality control in highly dynamic manufacturing lines, where products are added or removed from production. The structure of the vision-based measurement system is described. In particular, the stereo system is created by the movement of a single camera mounted on a six-axis manipulator. The visual software is structured in three phases: single camera and stereo calibration, addition of the visual inspection tasks for the object to be measured, stereo measurement. The feasibility of the proposed solution has been tested in a real industrial application with strong requirements on robot speed. The comparison of experimental results on a target with a simple and well-defined shape has shown that the proposed solution provides better results, in terms of accuracy and measurement speed, when compared to commercial libraries and RGBD vision systems. Besides, the robot motion policy adopted by the solution proposed here guarantees a continuous movement without the need for stop\&go phases. {\textcopyright} 2015, Springer Science+Business Media Dordrecht.

}, doi = {10.1007/s10846-015-0249-4}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84955089994\&partnerID=40\&md5=9c4abfb9d88e7dff178bbd704c850bbb}, author = {Fulvio, G.D. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti} } @article {Pierdicca201525, title = {Making visible the invisible. Augmented reality visualization for 3D reconstructions of archaeological sites}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {9254}, year = {2015}, note = {cited By 0}, pages = {25-37}, doi = {10.1007/978-3-319-22888-4_3}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84944688602\&partnerID=40\&md5=5988b5e6786fdbcd931f64ac7f8483f5}, author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Eva Savina Malinverni and F. Colosi and R. Orazi} } @conference {Tascini199518, title = {Model attraction in medical image object recognition}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {2436}, year = {1995}, note = {cited By 0}, pages = {18-29}, abstract = {This paper presents as new approach to image recognition based on a general attraction principle. A cognitive recognition is governed by a {\textquoteright}focus on attention{\textquoteright} process that concentrates on the visual data subset of task- relevant type only. Our model-based approach combines it with another process, focus on attraction, which concentrates on the transformations of visual data having relevance for the matching. The recognition process is characterized by an intentional evolution of the visual data. This chain of image transformations is viewed as driven by an attraction field that attempts to reduce the distance between the image-point and the model-point in the feature space. The field sources are determined during a learning phase, by supplying the system with a training set. The paper describes a medical interpretation case in the feature space, concerning human skin lesions. The samples of the training set, supplied by the dermatologists, allow the system to learn models of lesions in terms of features such as hue factor, asymmetry factor, and asperity factor. The comparison of the visual data with the model derives the trend of image transformations, allowing a better definition of the given image and its classification. The algorithms are implemented in C language on a PC equipped with Matrox Image Series IM-1280 acquisition and processing boards. The work is now in progress.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0029452584\&partnerID=40\&md5=181cb1d8753867d28066421ab8229a59}, author = {Guido Tascini and Primo Zingaretti} }