@conference {liciotti2017hmm,
	title = {HMM-based activity recognition with a ceiling RGB-D camera},
	booktitle = {ICPRAM (International Conference on Pattern Recognition Applications and Methods)},
	year = {2017},
	month = {02/2017},
	abstract = {<p>Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, the abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available.</p>
},
	author = {Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Bellotto, Nicola and Duckett, Tom}
}
@conference {Mancini2015534,
	title = {High-resolution mapping of river and estuary areas by using unmanned aerial and surface platforms},
	booktitle = {2015 International Conference on Unmanned Aircraft Systems, ICUAS 2015},
	year = {2015},
	note = {cited By 0},
	pages = {534-542},
	doi = {10.1109/ICUAS.2015.7152333},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84941111909\&partnerID=40\&md5=994c13558511f25e91843621dde17a5c},
	author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti and Sauro Longhi}
}
@conference {liciotti2015human,
	title = {Human activity analysis for in-home fall risk assessment},
	booktitle = {Communication Workshop (ICCW), 2015 IEEE International Conference on},
	year = {2015},
	pages = {284{\textendash}289},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Daniele Liciotti and Gionata Massi and Emanuele Frontoni and Adriano Mancini 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}
}
@article {Zingaretti2009500,
	title = {A hybrid approach to land cover classification from multi spectral images},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5716 LNCS},
	year = {2009},
	note = {cited By 5},
	pages = {500-508},
	abstract = {This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The specific objective of this paper is to describe a new methodology for producing really exploitable results from automatic classification algorithms. Input data are basically constituted by multispectral images, integrated with textural and contextual measures. The output is constituted by an image with each pixel assigned to one out of 15 classes at the second level of the CLC legend or let unclassified (somehow a better solution than a classification error), plus a stability map that helps users to separate the regions classified with high accuracy from those whose classification result should be verified before being used. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	doi = {10.1007/978-3-642-04146-4_54},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-76249123810\&partnerID=40\&md5=fcaeb5e46615d9bbdf96fcbf4ffac41a},
	author = {Primo Zingaretti and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini}
}
@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}
}
