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  • Skin Lesion Image Segmentation Using Delaunay Triangulation for Melanoma Detection. Andrea Pennisi, Domenico Daniele Bloisi, Daniele Nardi, Anna Rita Giampietruzzi, Chiara Mondino, Antonio Facchiano. Computerized Medical Imaging and Graphics, 2016.
    Abstract Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.
  • On-line Real-time Crowd Behavior Detection in Video Sequences. Andrea Pennisi, Domenico Daniele Bloisi, Luca Iocchi. Computer Vision and Image Understanding Journal (CVIU), pp. 166-176, 2016.
    Abstract Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach.
  • Melanoma Detection Using Delaunay Triangulation. Andrea Pennisi, Domenico Daniele Bloisi, Daniele Nardi, Anna Rita Giampetruzzi, Chiara Mondino and Antonio Facchiano. IEEE 27th International Conference on Tools with Artificial Intelligence, 2015.
    Abstract The detection of malignant lesions in dermoscopic images by using automatic diagnostic tools can help in reducing mortality from melanoma. In this paper, we describe a fully-automatic algorithm for skin lesion segmentation in dermoscopic images. The proposed approach is highly accurate when dealing with benign lesions, while the detection accuracy significantly decreases when melanoma images are segmented. This particular behavior lead us to consider geometrical and color features extracted from the output of our algorithm for classifying melanoma images, achieving promising results.
  • ARGOS-Venice Boat Classification. Domenico D. Bloisi, Luca Iocchi, Andrea Pennisi, and Luigi Tombolini. 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2015.
    Abstract Detection, tracking, and classification of people and ve-hicles are fundamental processes in intelligent surveillancesystems. The use of publicly available data set is the appro-priate way to compare the relative merits of existing meth-ods and to develop and assess new robust solutions. In thispaper, we focus on the maritime domain and we describe thegeneration of boat classification data sets, containing im-ages of boats automatically extracted by the ARGOS system,operating 24/7 in Venice (Italy). The data sets are uniquein their nature, since they come from an incomparable envi-ronment like Venice, but they present very interesting chal-lenges to vehicle classification, due to changes in the en-vironmental conditions, boat wakes, waves, reflections, etc.We thus believe that robust techniques, validated throughthe ARGOS Boat Classification data sets, will improve thedevelopment and deployment of solutions in similar appli-cations related to vehicle detection and classification.
  • Real-Time Adaptive Background Modeling in Fast Changing Conditions. Andrea Pennisi, Fabio Previtali, Domenico D. Bloisi, and Luca Iocchi. 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2015.
    Abstract Background modeling in fast changing scenarios is a challenging task due to unexpected events like sudden illumination changes, reflections, and shadows, which can strongly affect the accuracy of the foreground detection. In this paper, we describe a real-time and effective background modeling approach, called FAFEX, that can deal with global and rapid changes in the scene background. The method is designed to identify variations in the background geometry of the monitored scene and it has been quantitatively tested on a publicly available data set, containing a varied set of highly dynamic environments. The experimental evaluation demonstrates how our method is able to effectively deals with challenging sequences in real-time.
  • Multi-modal Background Model Initialization. Domenico D. Bloisi, Alfonso Grillo, Andrea Pennisi, Luca Iocchi, and Claudio Passaretti. New Trends in Image Analysis and Processing, ICIAP 2015 Workshops, 2015.
    Abstract Background subtraction is a widely used technique for detecting moving objects in image sequences. Very often background subtraction approaches assume the availability of one or more clear frames (i.e., without foreground objects) at the beginning of the image sequence in input. This strong assumption is not always correct, especially when dealing with dynamic background. In this paper, we present the results of an on-line and real-time background initialization method, called IMBS, which generates a reliable initial background model even if no clear frames are available. The accuracy of the proposed approach is calculated on a set of seven publicly available benchmark sequences. Experimental results demonstrate that IMBS generates accurate background models with respect to eight different quality metrics.
  • Multi-robot Surveillance Through a Distributed Sensor Network. Andrea Pennisi, Fabio Previtali, Cristiano Gennari, Domenico D. Bloisi, Luca Iocchi, Francesco Ficarola, Andrea Vitaletti, Daniele Nardi. Chapter in Cooperative Robots and Sensor Networks 2015, Springer International Publishing, vol. 604, pp. 77-98, 2015
    Abstract Automatic surveillance of public areas, such as airports, trainstations, and shopping malls, requires the capacity of detecting and rec-ognizing possible abnormal situations in populated environments. In thisbook chapter, an architecture for intelligent surveillance in indoor pub-lic spaces, based on an integration ofinteractiveandnon-interactiveheterogeneous sensors, is described. As a difference with respect to tra-ditional, passive and pure vision-based systems, the proposed approachrelies on a distributed sensor network combining RFID tags, multiplemobile robots, and fixed RGBD cameras. The presence and the positionof people in the scene is detected by suitably combining data comingfrom the sensor nodes, including those mounted on board of the mobilerobots that are in charge of patrolling the environment. The robots canadapt their behavior according to the current situation, on the basis of aPrey-Predator scheme, and can coordinate their actions to fulfill the re-quired tasks. Experimental results have been carried out both on real andon simulated data to show the effectiveness of the proposed approach.
  • Distributed sensor network for multi-robot surveillance. Andrea Pennisi, Fabio Previtali, Francesco Ficarola, Domenico Daniee Bloisi, Luca Iocchi, and Andrea Vitaletti. Procedia Computer Science, 32(0):1095 – 1100, 2014.
    Abstract Monitoring of populated indoor environments is crucial for the surveillance of public spaces like airports or embassies, wherethe behaviorof people may be relevant in order to determine abnormal situations. In this paper, a surveillance system based onan integration ofinteractiveandnon-interactiveheterogeneous sensorsis described. As a difference with respect to traditional,pure vision-based systems, the proposed approach relieson Radio Frequency Identification (RFID) tags carried by people, multiplemobile robots (each one equipped witha laser range finder and an RFID reader), and fixed RGBD cameras. The main task of thesystemis to assess the presence and the position of people in the environment. This is obtained by suitably integrating data comingfrom heterogeneous sensors, including those mountedon board of mobile robots that are in charge of patrolling the environment.The robots also adapt their behavior accordingto the current situation, on the basis of a Prey-Predator scheme. Experimental resultscarried out bothon real and on simulated data show the effectiveness of the approach.
  • Novel patterns and methods for zooming camera calibration. Andrea Pennisi, Domenico Daniele Bloisi, Claudio Gaz, Luca Iocchi, and Daniele Nardi. Journal of WSCG, 21(1):59–67, 2013.
    Abstract Camera calibration is a necessary step in order to develop applications that need to establish a relationship between image pixels and real world points. The goal of camera calibration is to estimate the extrinsic and intrinsic camera parameters. Usually, for non-zooming cameras, the calibration is carried out by using a grid pattern of known dimensions (e.g., a chessboard). However, for cameras with zoom functions, the use of a grid pattern only is not sufficient, because the calibration has to be effective at multiple zoom levels and some features (e.g., corners) could not be detectable. In this paper, a calibration method based on two novel calibration patterns, specifically designed for zooming cameras, is presented. The first pattern, called in-lab pattern, is designed for intrinsic parameter recovery, while the second one, called on-field pattern, is conceived for extrinsic parameter estimation. As an application example, on-line virtual advertising in sport events, where the objective is to insert virtual advertising images into live or pre-recorded television shows, is considered. A quantitative experimental evaluation shows an increase of performance with respect to the use of standard calibration routines considering both re-projection accuracy and calibration time.
  • Ground truth acquisition of humanoid soccer robot behaviour. Andrea Pennisi, Domenico Daniele Bloisi, Luca Iocchi, and Daniele Nardi. In Proceedings of the 17th Annual Robocup International Symposium, pages 1–8, 2013.
    Abstract In this paper an open source software for monitoring humanoid soccer robot behaviours is presented. The software is part of an easy to set up system, conceived for registering ground truth data that can be used for evaluating and testing methods such as robot coordination and localization. The hardware architecture of the system is designed for using multiple low-cost visual sensors (four Kinects). The software includes a foreground computation module and a detection unit for both players and ball. A graphical user interface has been developed in order to facilitate the creation of a shared multi-camera plan view, in which the observations of players and ball are re-projected to obtain global positions. The effectiveness of the implemented system has been proven using a laser sensor to measure the exact position of the objects of interest in the field.
  • Background modeling in the maritime domain. Domenico Daniele Bloisi, Andrea Pennisi, and Luca Iocchi. Machine Vision and Applications, pages 1–13, 2013.
    Abstract Maritime environment represents a challeng-ing scenario for automatic video surveillance, due to thecomplexity of the observed scene: waves on the watersurface, boat wakes, and weather issues contribute togenerate a highly dynamic background. Moreover, anappropriate background model has to deal with gradualand sudden illumination changes, camera jitter, shad-ows, and reflections that can provoke false detections.Using a predefined distribution (e.g., Gaussian) for gen-erating the background model can result ineffective, dueto the need of modeling non-regular patterns. In thispaper, a method for creating a “discretization” of anunknown distribution that can model highly dynamicbackground such as water is described. A quantitativeevaluation carried out on two publicly available datasets of videos and images, containing data recordedin different maritime scenarios, with varying light andweather conditions, demonstrates the effectiveness ofthe approach.
  • Human-robot collaboration for semantic labeling of the environment. Taigo Maria Bonanni, Andrea Pennisi, Domenico Daniele Bloisi, Luca Iocchi, and Daniele Nardi. In Proceedings of the 3rd Workshop on Semantic Perception, Mapping and Exploration (SPME), pages 1–6, 2013.
    Abstract Today’s robots are able to perform more and more complex tasks, which usually require a high degree of interaction with the environment they have to operate in. As a consequence, robotic systems should have a deep and specific knowledge of their workspaces, which goes far beyond a simple metric representation a robotic system can build up through SLAM (Simultaneous Localization and Mapping). In this paper, we present a novel human-robot collaboration approach, designed to extract 3D shapes associated to objects of interest pointed out by a human operator. The information regarding the segmented objects are then integrated into a metric map, built by the robot, providing a high-level representation of the environment that embodies all the knowledge required by a robot to actually execute complex tasks.
  • Context-aware video analysis for infomobility. Luca Iocchi and Andrea Pennisi. In Proceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), CISIS 2012, pages 971–976. IEEE Computer Society, 2012.
    Abstract Mobility in large touristic cities (such as Rome and Venice), where needs of citizen and tourists are different(and sometimes even conflicting), is a very relevant problem and infomobility is thus increasingly important. Since active technologies, requiring the passengers to wear some devices(e.g., RFID devices) are not commonly available and cannot be enforced on citizens and tourists, a complete passive sensor system is needed. In this paper we describe development and experimentation of techniques for human activity recognition for infomobility applications based on 3D data extracted from stereo and Kinect cameras. More specifically, we considered the problem of automatic estimation of the number of people present in a bus stop area in a crowded city, like Venice and experimented an approach integrating 3D data analysis, feature extraction and machine learning techniques. Results assessing the feasibility and performance of the proposed approaches are also presented in this paper.