@conference {pierdicca2015low,
	title = {Low cost embedded system for increasing retail environment intelligence},
	booktitle = {Multimedia \& Expo Workshops (ICMEW), 2015 IEEE International Conference on},
	year = {2015},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Roberto Pierdicca and Daniele Liciotti and Marco Contigiani and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@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}
}
@conference {Carbonaro1997147,
	title = {Landmark matching in a varying environment},
	booktitle = {Proceedings of the Euromicro Workshop on Advanced Mobile Robots, EUROBOT},
	year = {1997},
	note = {cited By 4},
	pages = {147-153},
	abstract = {A system for landmark tracking by a template matching approach is described. Route following based on landmarks may require many models to cover all different situations, so a genetic algorithm learning technique is used to adapt modelling parameters to environmental conditions (lighting, shadows, reflexes, etc.) during the tracking. In addition, the mobile robot self-localization is obtained by a stereo approach that uses the centres of matching in the two images to solve in a simple way the correspondence analysis in the 3-D position estimation. The experimental results show that the tracking robustness is improved when the adaptive template matching is used for landmark tracking.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0031356261\&partnerID=40\&md5=84da6f7845bf77d41951a6da2296155c},
	author = {A. Carbonaro and Primo Zingaretti}
}
