@proceedings {, title = {A low-cost and low-burden secure solution to track small-scale fisheries}, journal = {Conference: 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea)}, year = {2021}, month = {11/2021}, pages = {382-387}, abstract = {

During the last decade accurate spatial and quantitative information of industrial fisheries have been increasingly given using tracking technologies and machine learning analytical algorithms. However, in most small-scale fisheries, lack of spatial data has been a recurrent bottleneck as Vessel Monitoring System and Automatic Identification System, developed for vessels longer than 12 and 15 m in length respectively, have little applicability in these contexts. It follows that small-scale vessels (\< 12 m in length) remain untracked and largely unregulated, even though they account for most of the fishing fleet in operation in the Mediterranean Sea. As such, the tracking of small-scale fleets tends to require the use of novel and low cost solutions that could be addressed by small vessels often without dedicated electrical systems. In this paper we propose a scalable architecture that makes use of a low-cost LoRaWAN/cellular network to acquire and process positioning data from small-scale vessels; preliminary results of a first installation of the prototype are presented, as well as the data collected. The emergence of a such low-cost and open source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management, and cross-border marine spatial planning.

}, keywords = {cloud computing, fleet management system, maritime communication, small-scale fisheries, vessel position data}, doi = {https://doi.org/10.1109/MetroSea52177.2021.9611622}, url = {https://ieeexplore.ieee.org/document/9611622}, author = {Anna Nora Tassetti and Alessandro Galdelli and Jacopo Pulcinella} } @article {, title = {A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities}, journal = {Sensors}, volume = {21}, year = {2021}, month = {04/2021}, abstract = {

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight {\textquotedblleft}suspicious{\textquotedblright} AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel{\textemdash}and the gear it adopts{\textemdash}is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.

}, keywords = {Automatic Identification System, Machine Learning, maritime surveillance, Synthetic Aperture Radar data integration}, issn = {1424-8220}, doi = {10.3390/s21082756}, url = {https://www.mdpi.com/1424-8220/21/8/2756}, author = {Alessandro Galdelli and Adriano Mancini and Carmen Ferra Vega and Anna Nora Tassetti} } @article {, title = {Faster R-CNN approach for detection and quantification of DNA damage in comet assay images}, journal = {Computers in Biology and Medicine}, volume = {123}, year = {2020}, pages = {103912}, author = {Rosati, Riccardo and Romeo, Luca and Silvestri, Sonia and Marcheggiani, Fabio and Tiano, Luca and Frontoni, Emanuele} } @article {, title = {SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0}, journal = {Information}, volume = {11}, year = {2020}, pages = {202}, author = {Calabrese, Matteo and Cimmino, Martin and Fiume, Francesca and Manfrin, Martina and Romeo, Luca and Ceccacci, Silvia and Paolanti, Marina and Toscano, Giuseppe and Ciandrini, Giovanni and Carrotta, Alberto and others} } @conference {, title = {A Cloud Computing Architecture to Map Trawling Activities Using Positioning Data}, year = {2019}, abstract = {

Descriptive and spatially-explicit information on fisheries plays a key role for an efficient integrated management of the maritime activities and the sustainable use of marine resources. However, this information is today still hard to obtain and, consequently, is a major issue for implementing Marine Spatial Planning (MSP). Since 2002, the Automatic Identification System (AIS) has been undergoing a major development allowing now for a real time geo-tracking and identification of equipped vessels of more than 15m in length overall (LOA) and, if properly processed, for the production of adequate information for MSP. Such monitoring systems or other low-cost and low-burden solutions are still missing for small vessels (LOA \< 12m), whose catches and fishing effort remain spatially unassessed and, hence, unregulated. In this context, we propose an architecture to process vessel tracking data, understand the behaviour of trawling fleets and map related fishing activities. It could be used to process not only AIS data but also positioning data from other low cost systems as IoT sensors that share their position over LoRa and 2G/3G/4G links. Analysis gives back important and verified data (overall accuracy of 92\% for trawlers) and opens up development perspectives for monitoring small scale fisheries, helping hence to fill fishery data gaps and obtain a clearer picture of the fishing grounds as a whole.

}, doi = {10.1115/DETC2019-97779}, url = {https://doi.org/10.1115/DETC2019-97779}, author = {Alessandro Galdelli and Adriano Mancini and Anna Nora Tassetti and Carmen Ferra Vega and Enrico Armelloni and Giuseppe Scarcella and Gianna Fabi and Primo Zingaretti} } @article {sturari2017integrating, title = {Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping}, journal = {European Journal of Remote Sensing}, volume = {50}, number = {1}, year = {2017}, pages = {1{\textendash}17}, publisher = {Taylor \& Francis}, author = {Mirco Sturari and Emanuele Frontoni and Roberto Pierdicca and Adriano Mancini and Eva Savina Malinverni and Anna Nora Tassetti and Primo Zingaretti} } @article {pierdicca2016smart, title = {Smart maintenance of riverbanks using a standard data layer and Augmented Reality}, journal = {Computers \& Geosciences}, volume = {95}, year = {2016}, pages = {67{\textendash}74}, publisher = {Pergamon}, author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Adriano Mancini and Eva Savina Malinverni and Anna Nora Tassetti and Marcheggiani, Ernesto and Galli, Andrea} } @conference {ercoli2015measurement, title = {A measurement procedure for the assessment of thermoregulatory activitity in premature babies}, booktitle = {Medical Measurements and Applications (MeMeA), 2015 IEEE International Symposium on}, year = {2015}, pages = {229{\textendash}233}, publisher = {IEEE}, organization = {IEEE}, author = {Ercoli, Ilaria and Scalise, Lorenzo and Annalisa Cenci and Marchionni, Paolo and Enrico Primo Tomasini and Virgilio Paolo Carnielli} } @article {Mancini2013409, title = {A novel method for fast processing of large remote sensed image}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8157 LNCS}, number = {PART 2}, year = {2013}, note = {cited By 1}, pages = {409-418}, abstract = {In this paper we present a novel approach to reduce the computational load of a CFAR detector. The proposed approach is based on the use of integral images to directly manage the presence of masked pixels or invalid data and reduce the computational time. The approach goes through the challenging problem of ship detection from remote sensed data. The capability of fast image processing allows to monitor the marine traffic and identify possible threats. The approach allows to significantly boost the performance up to 50x working with very high resolution image and large kernels. {\textcopyright} 2013 Springer-Verlag.}, doi = {10.1007/978-3-642-41184-7_42}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84884709393\&partnerID=40\&md5=335f37537f59723ffcfa04255b21821b}, author = {Adriano Mancini and Anna Nora Tassetti and Cinnirella, A. and Emanuele Frontoni and Primo Zingaretti} } @article {Malinverni20111025, title = {Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery}, journal = {International Journal of Geographical Information Science}, volume = {25}, number = {6}, year = {2011}, note = {cited By 13}, pages = {1025-1043}, abstract = {Traditionally, remote sensing has employed pixel-based classification techniques to deal with land use/land cover (LULC) studies. Generally, pixel-based approaches have been proven to work well with low spatial resolution imagery (e.g. Landsat or System Pour L{\textquoteright}Observation de la Terre sensors). Now, however, commercially available high spatial resolution images (e.g. aerial Leica ADS40 and Vexcel UltraCam sensors, and satellite IKONOS, Quickbird, GeoEye and WorldView sensors) can be problematic for pixel-based analysis due to their tendency to oversample the scene. This is driving research towards object-based approaches. This article proposes a hybrid classification method with the aim of incorporating the advantages of supervised pixel-based classification into object-based approaches. The method has been developed for medium- scale (1:10,000) LULC mapping using ADS40 imagery with 1 m ground sampling distance. First, spatial information is incorporated into a pixel-based classification (AdaBoost classifier) by means of additional texture features (Haralick, Gabor, Law features), which can be selected {\textquoteright}ad hoc{\textquoteright} according to optimal training samples ({\textquoteright}Relief-F{\textquoteright} pproach,Mahalanobis distances). Then a rule-based approach sorts segmented regions into thematic CORINE Land Cover classes in terms of membership class percentages (a modified Winner-Takes-All approach) and shape parameters. Finally, ancillary data (roads, rivers, etc.) are exploited to increase classification accuracy. The experimental results show that the proposed hybrid approach allows the extraction of more LULC classes than conventional pixel-based methods, while improving classification accuracy considerably. A second contribution of this article is the assessment of classification reliability by implementing a stability map, in addition to confusion matrices. {\textcopyright} 2011 Taylor \& Francis.}, doi = {10.1080/13658816.2011.566569}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685342\&partnerID=40\&md5=18767c8a88bf2abff53ef96ec138f0aa}, author = {Eva Savina Malinverni and Anna Nora Tassetti and Adriano Mancini and Primo Zingaretti and Emanuele Frontoni and A. Bernardini} } @conference {Malinverni20102836, title = {LCLU Information System for object-oriented nomenclature}, booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)}, year = {2010}, note = {cited By 0}, pages = {2836-2839}, abstract = {A Land Cover/Land Use (LCLU) Information System is proposed as a new dynamic and flexible approach to describe landscape objects. It is able to give a deeper and more realistic thematic description by storing membership land cover attributes for each polygon automatically extracted and classified by the T-MAP software. The proposed approach can overcome the traditional "hard" classification by taking directly into account "fuzzy" cover components and making the classification approach more bounded with the polygon characteristics and their changes. The LCLU Information System can be easily integrated with different databases, making it suitable for different nomenclatures and further analysis, regarding environmental indexes, class updating and classification stability assessment. {\textcopyright} 2010 IEEE.}, doi = {10.1109/IGARSS.2010.5651398}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650884639\&partnerID=40\&md5=a38544ae56c5f11edee2b11e7730439c}, author = {Eva Savina Malinverni and Anna Nora Tassetti and Primo Zingaretti} } @article {Bernardini201043, title = {Pixel, object and hybrid classification comparisons}, journal = {Journal of Spatial Science}, volume = {55}, number = {1}, year = {2010}, note = {cited By 3}, pages = {43-54}, abstract = {The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit. The generalized GIS-ready products derived from the results of pixel-based approaches and the availability of higherresolution imagery have directed research towards object-based classification approaches. In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover information derived from a pixel classification by means of the Winner Takes All (WTA) algorithm. Land use and land cover results obtained through this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach. {\textcopyright} 2010 Surveying and Spatial Sciences Institute and Mapping Sciences Institute, Australia.}, doi = {10.1080/14498596.2010.487641}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052137947\&partnerID=40\&md5=74b7a4fe82987b5213c27b7315b0f768}, author = {A. Bernardini and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini and Anna Nora Tassetti and Primo Zingaretti} } @conference {Montesanto2007229, title = {Fingerprints recognition using minutiae extraction: A fuzzy approach}, booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007}, year = {2007}, note = {cited By 0}, pages = {229-234}, abstract = {The aim of this paper is to study the fingerprint verification based on local ridge discontinuities features (minutiae) only using grey scale images. We extract minutiae using two algorithms those following ridge lines and then recording ridge endings and bifurcations. Moreover we use a third algorithm able to develop a minutiae verification processing a local area using a neural network ( multilayer perceptron). Fingerprint distortion is filtered using a minutiae whole representation based on regular invariant moments. The results of the three minutiae extraction algorithms are joined during the minutiae pattern matching phase for fingerprint verification. Here we propose a new method of matching that use fuzzy operator to bypass the problem of different numbers of minutiae extracted from the algorithms. Experimental evidences show fingerprint recognition up to 95\%. {\textcopyright} 2007 IEEE.}, doi = {10.1109/ICIAP.2007.4362784}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-48149113403\&partnerID=40\&md5=01f07886247083f3db66c7477c334984}, author = {A. Montesanto and Paola Baldassarri and Vallesi, G. and Guido Tascini} } @article {Montesanto200684, title = {Navigation with memory in a partially observable environment}, journal = {Robotics and Autonomous Systems}, volume = {54}, number = {1}, year = {2006}, note = {cited By 3}, pages = {84-94}, abstract = {The paper presents an architecture that allows the reactive visual navigation via an unsupervised reinforcement learning. This objective is reached using Q-learning and a hierarchical approach to the developed architecture. Using these techniques requires a deviation from the Partially Observable Markov Decision Processes (POMDP) and some innovations: heuristic techniques for generalizing the experience and for treating the partial observability; a technique for the speed adjournment of the Q function; the definition of a special reinforcement policy adequate for learning a complex task without supervision. The result is a satisfactory learning of the navigation assignment in a simulated environment. {\textcopyright} 2005 Elsevier B.V. All rights reserved.}, doi = {10.1016/j.robot.2005.09.015}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-29344460057\&partnerID=40\&md5=a3f0d1229db6d5c8658cd509ca5acf81}, author = {A. Montesanto and Guido Tascini and Paolo Puliti and Paola Baldassarri} } @article {Baldassarri2003201, title = {Self-organizing maps versus growing neural gas in a robotic application}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {2687}, year = {2003}, note = {cited By 1}, pages = {201-208}, abstract = {The paper proposes a method for visual based self-localisation of a mobile agent in indoor environment. The images acquired by the camera constitute an implicit topological representation of the environment. The environment is a priori unknown and so the implemented architecture is entirely unsupervised. To compare the performance of some self-organising neural networks, a similar neural network architecture of both Self-Organizing Map (SOM) and Growing Neural Gas (GNG) has been realized. Extensive simulations are provided to characterise the effectiveness of the GNG model in recognition speed, classification tasks and in particular topology preserving as compared to the SOM model. This behaviour depends on the following fact: a network (GNG) that adds nodes into map space can approximate the input space more accurately than a network with a predefined structure and size (SOM). The work shows that the GNG network is able to correctly reconstruct the environment topological map. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35248829969\&partnerID=40\&md5=28fe2f7301103d80e28ff664c2a74906}, author = {Paola Baldassarri and Paolo Puliti and A. Montesanto and Guido Tascini} } @conference {Baldassarri2003368, title = {Visual self-localisation using automatic topology construction}, booktitle = {Proceedings - 12th International Conference on Image Analysis and Processing, ICIAP 2003}, year = {2003}, note = {cited By 0}, pages = {368-374}, abstract = {The paper proposes a machine learning method for self-localising a mobile agent, using the images supplied by a single omni-directional camera. The images acquired by the camera may be viewed as an implicit topological representation of the environment. The environment is a priori unknown and the topological representation is derived by unsupervised neural network architecture. The architecture includes a self-organising neural network, and is constituted by a growing neural gas, which is well known for its topology preserving quality. The growth depends on the topology that is not a priori defined, and on the need of discovering it, by the neural network, during the learning. The implemented system is able to recognise correctly the input frames and to reconstruct a topological map of the environment. Each node of the neural network identifies a single zone of the environment and the connections between the nodes correspond to the real space connections in the environment. {\textcopyright} 2003 IEEE.}, doi = {10.1109/ICIAP.2003.1234077}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890545853\&partnerID=40\&md5=6954a4c9b47cdb3e275eac96afdf95a2}, author = {Paola Baldassarri and Paolo Puliti and A. Montesanto and Guido Tascini} } @conference {Tascini1998278, title = {Unauthorized access identification in restricted areas}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {3364}, year = {1998}, note = {cited By 0}, pages = {278-286}, abstract = {The paper describes a system to control vehicle accesses in restricted areas. The signalling of vehicles whose license- plates do not belong to a specific database is the aim of the system. The adaptation to different environmental conditions, and the identification of a vehicle by processing the license- plate pattern as a whole, without considering the recognition of the characters, are its two main characteristics. The system implements a recognition engine constituted by two modules. First, the system analyzes the video-recorded sequences to select a frame in which the license-plate satisfies pre-defined constraints, and extracts the license- plate template on which the matching with the model templates stored in the database will be performed. Second, vehicle identification is performed by a genetic template matching that, without requiring a high computational complexity, provides adaptation to normal environmental variations by exploiting learning capabilities. The implemented system, forced to distinguish only between authorized and unauthorized vehicles according to a threshold in the genetic fitness function, shows robust performance on Italian cars, but it is adaptable to different license-plate models, and is independent from outdoor conditions. {\textcopyright}2003 Copyright SPIE - The International Society for Optical Engineering.}, doi = {10.1117/12.317481}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-62249155465\&partnerID=40\&md5=c5bfb42de0062e33b5145339e0517adb}, author = {Guido Tascini and A. Carbonaro and Primo Zingaretti} } @conference {Conte19961213, title = {Automatic analysis of visual data in submarine pipeline inspection}, booktitle = {Oceans Conference Record (IEEE)}, volume = {3}, year = {1996}, note = {cited By 8}, pages = {1213-1219}, abstract = {An automatic system for analysis and interpretation of visual data from submarine pipeline inspection operates using images collected by cameras mounted on an unmanned Remotely Operated Vehicle (ROV) that moves along the pipeline. The system supports the operator in navigating the ROV and in detecting structural elements of the pipeline such as anodes, gravel heaps and reference bands. The system employs a feature based technique for recognizing the pipeline{\textquoteright}s profile and for detecting the structural elements. Information gathered by analyzing and interpreting the visual data can be used for automatic guidance and inspection by integrating the system into a larger architecture.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0030385353\&partnerID=40\&md5=d5723a92ebcff100f3868adc25135935}, author = {Conte, G. and S.M. Zanoli and Perdon, A.M. and Guido Tascini and Primo Zingaretti} } @conference {Zingaretti1996129, title = {Imaging approach to real-time tracking of submarine pipeline}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {2661}, year = {1996}, note = {cited By 10}, pages = {129-137}, abstract = {The work presents a real-time underwater imaging system for identification and tracking of a submarine pipeline on a sequence of recorded images. The main novelty of this work relies on adopting an automatic approach that is entirely based on the analysis and interpretation of visual data, in spite of the various limitations upon the ability to image underwater objects. The analysis of the data is performed starting from image processing operations (like filtering, profile analysis, feature enhancement) implemented on a dedicated board. Then, the system employs an efficient dynamic process for recognizing the two contours of the pipeline. In each frame the system is able to determine the equations of the two straight lines corresponding to the pipeline contours. The system reaches satisfactory performances in real time operation: up to eight frames per second on a Pentium based PC. The results of this work are somewhat more meaningful as the input images were acquired by three cameras, mounted on a remotely operated vehicle travelling at one nautical mile an hour, without any attention either to illumination conditions or stability of cameras. This work is originated from the interest of Snamprogetti in enhancing the level of automation in submarine pipeline inspection.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0029754848\&partnerID=40\&md5=65001780f0cb94776ceeb1da3a6eec40}, author = {Primo Zingaretti and Guido Tascini and Paolo Puliti and S.M. Zanoli} } @article {Tascini1996432, title = {Real-time inspection by submarine images}, journal = {Journal of Electronic Imaging}, volume = {5}, number = {4}, year = {1996}, note = {cited By 5}, pages = {432-442}, abstract = {A real-time application of computer vision concerning tracking and inspection of a submarine pipeline is described. The objective is to develop automatic procedures for supporting human operators in the real-time analysis of images acquired by means of cameras mounted on underwater remotely operated vehicles (ROV). Implementation of such procedures gives rise to a human-machine system for underwater pipeline inspection that can automatically detect and signal the presence of the pipe, of its structural or accessory elements, and of dangerous or alien objects in its neighborhood. The possibility of modifying the image acquisition rate in the simulations performed on video-recorded images is used to prove that the system performs all necessary processing with an acceptable robustness working in real-time up to a speed of about 2.5 kn, widely greater than that the actual ROVs and the security features allow. {\textcopyright} 1996 SPIE and IS\&T.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0009091679\&partnerID=40\&md5=0be09172d776524c453eb82b24820faf}, author = {Guido Tascini and Primo Zingaretti and Conte, G.} } @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} } @conference {Tascini19942378, title = {Attraction based recognition}, booktitle = {Proceedings of the IEEE International Conference on Systems, Man and Cybernetics}, volume = {3}, year = {1994}, note = {cited By 0}, pages = {2378-2383}, abstract = {An approach to image recognition is proposed, which is achieved through the correspondences between predicted and measured properties: points, lines, regions, color, shape, etc. Image parts are subjected to a series of transformations performed by the recognition process. In these transformations, called image chaining, there is a grouping of image parts or features due to a field continually perturbed by the recognition process. A fundamental role is played by the spatial correspondences together with an intentional behavior of the transformed visual data called attraction. After a definition of the attraction principle, the paper describes the attraction process, and justifies the image chaining concept. Finally, a series of processes - segmentation refinement, search space reduction, and perceptual organization - are interpreted in terms of the attraction principle.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0028742684\&partnerID=40\&md5=9925689ed71ffc413fb731f2205f4173}, author = {Guido Tascini and Primo Zingaretti} } @conference {Tascini19941722, title = {Automatic quantitative analysis of lumbar bone radiographs}, booktitle = {IEEE Nuclear Science Symposium \& Medical Imaging Conference}, number = {pt 3}, year = {1994}, note = {cited By 1}, pages = {1722-1726}, abstract = {In the current radiological diagnosis the radiographs often appear as {\textquoteleft}rough{\textquoteright} means to be understood due to the bad quality of radiographic images. The aim of the work is to guarantee a diagnostic support even in presence of radiographic means only, with a little cost increment due to a computer system, by supplying a useful primary screening tool that automatically analyzes digitized radiographs and gives important features relevant from medical standpoint. The specific domain of image analysis concerns the osteoporosis which is a long term diseases requiring an accurate measurement of bone density and a periodic follow-up. The automatic image processing method described in the work goes through a series of steps: selection of the region of interest, first preprocessing, extraction of the vertebral left side, second preprocessing, extraction of upper and lower borders of the end plates, detection of the greatest uniform area inside the vertebral body, third preprocessing, correction of image disuniformity, detection of the end plates, feature detection (variance, mean-gray, concavity, body and tissue density, etc.). The automation of the process is complete and the results good agree with those obtained from human analysis.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0028277067\&partnerID=40\&md5=7871b50c44eb8cbdb7e70759b9b2eb0d}, author = {Guido Tascini and Primo Zingaretti} } @conference {Tascini1994838, title = {Image sequence recognition}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {2308}, number = {p 2}, year = {1994}, note = {cited By 1}, pages = {838-847}, abstract = {Image sequence recognition is a problem that occurs in computer vision and particularly in mobile robot vision. The feature based method has been selected to solve this problem. The method first extracts the features as corners, points of curvature, lines etc. Then the correspondence of these features is established between two successive frames, and finally motion parameters and object structure from correspondences are computed. Test results demonstrating the method are included.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0028735739\&partnerID=40\&md5=2752672a7fe70d15fb9df66474f82faf}, author = {Guido Tascini and Primo Zingaretti} } @conference {Tascini1993126, title = {Handwritten character recognition using background analysis}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {1906}, year = {1993}, note = {cited By 0}, pages = {126-133}, abstract = {The paper describes a low-cost handwritten character recognizer. It is constituted by three modules: the {\textquoteleft}acquisition{\textquoteright} module, the {\textquoteleft}binarization{\textquoteright} module, and the {\textquoteleft}core{\textquoteright} module. The core module can be logically partitioned into six steps: character dilation, character circumscription, region and {\textquoteleft}profile{\textquoteright} analysis, {\textquoteleft}cut{\textquoteright} analysis, decision tree descent, and result validation. Firstly, it reduces the resolution of the binarized regions and detects the minimum rectangle (MR) which encloses the character; the MR partitions the background into regions that surround the character or are enclosed by it, and allows it to define features as {\textquoteleft}profiles{\textquoteright} and {\textquoteleft}cuts;{\textquoteright} a {\textquoteleft}profile{\textquoteright} is the set of vertical or horizontal minimum distances between a side of the MR and the character itself; a {\textquoteleft}cut{\textquoteright} is a vertical or horizontal image segment delimited by the MR. Then, the core module classifies the character by descending along the decision tree on the basis of the analysis of regions around the character, in particular of the {\textquoteleft}profiles{\textquoteright} and {\textquoteleft}cuts,{\textquoteright} and without using context information. Finally, it recognizes the character or reactivates the core module by analyzing validation test results. The recognizer is largely insensible to character discontinuity and is able to detect Arabic numerals and English alphabet capital letters. The recognition rate of a 32 {\texttimes} 32 pixel character is of about 97\% after the first iteration, and of over 98\% after the second iteration.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0027277522\&partnerID=40\&md5=06fd7da850b665db5896240114bab4e7}, author = {Guido Tascini and Paolo Puliti and Primo Zingaretti} } @conference {Tascini1993322, title = {Retina vascular network recognition}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {1898}, year = {1993}, note = {cited By 5}, pages = {322-329}, abstract = {The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0027873308\&partnerID=40\&md5=fe51e921b3d6ecbbbd0483a977269acd}, author = {Guido Tascini and Passerini, G. and Paolo Puliti and Primo Zingaretti} } @conference {Tascini1991178, title = {Decision support system for capillaroscopic images}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {1450}, year = {1991}, note = {cited By 1}, pages = {178-185}, abstract = {The aim of the paper is to describe a decision support system operating in the area of capillaroscopic images. The system automatically sites the capillaroscopic analyzed image into one of the following classes: normal, diabetic and sclerodermic. The automatic morphometric analysis attempts to imitate physician behaviour and requires the introduction of some particular features connected with the specific domain. These features allow achieving a symbolic representation of the capillary partitioning it into three components: apex, arteriolar and venular side. The system is hierarchically organized in two levels. The system has been successfully used for obtaining images of nailfold capillaries of human finger.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0025798390\&partnerID=40\&md5=05b1aa9225d95f1ed2cecdc0a5268c2a}, author = {Guido Tascini and Paolo Puliti and Primo Zingaretti} } @article {Palareti1988307, title = {Prolong approach to image segmentation}, journal = {Applied Artificial Intelligence}, volume = {2}, number = {3-4}, year = {1988}, note = {cited By 0}, pages = {307-331}, abstract = {Segmentation is a problem in computer vision. It attempts to supply primitives for higher-level processes of interpretation. A multithreshold approach has already given acceptable results. This paper presents a Prolog implementation of a multithreshold system. The algorithms implemented concern image representation, connected regions, identification, and contour detection. The improvement of algorithms in a logic language rather than in a procedural one appeared to be of particular interest. The main reasons for this are implementation facility and the natural use of logic programming in the field of knowledge-based systems. In fact, a knowledge-based approach seems a reasonable solution to problems of assisted image understanding processes.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0024019798\&partnerID=40\&md5=bbf1d6d0aad82eee5540c0841df3bfad}, author = {Palareti, Aldopaolo and Paolo Puliti and Guido Tascini and Primo Zingaretti} }