Bianconi Francesco
Received the M.Eng. degree from the University of Perugia (Italy) and the Ph.D. in Computer-aided Design from a consortium of Italian universities. He has been visiting researcher at the University of Vigo (Spain) and the University of East Anglia (UK). Currently, he is Lecturer within the Department of Engineering of the University of Perugia. His research interests include computer vision, image processing and pattern recognition, with a special focus on texture and colour analysis. He is IEEE senior member.
Website URL: http://dismac.dii.unipg.it/bianco
Additional Details
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Phone075 585 3706
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RoleProfessore associato - Associate professor
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AreaProgettazione industriale, costruzioni meccaniche e metallurgia - Engineering design and metallurgy
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Curriculum Vitae
Wednesday, 30 April 2014 18:30
Greenhouse classification from satellite imagery
Stereo pair images from very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) can be succesfully used for land cover classification agricultural areas through object-based image analysis. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, proved to be the most important feature for greenhouse classification. We obtained overall accuracy close to 90%.
Source:
M.Á. Aguilar, F. Bianconi, F.J. Aguilar and I. Fernández, "Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery", Remote Sensing, 6(5):3554-3582, 2014
M.Á. Aguilar, F. Bianconi, F.J. Aguilar and I. Fernández, "Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery", Remote Sensing, 6(5):3554-3582, 2014
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Wednesday, 05 March 2014 10:43
Colour descriptors for parquet sorting
We have experimentally investigated and compared the performance of various colour descriptors (i.e.: soft descriptors, percentiles, marginal histograms and 3D histogram), and colour spaces (i.e.: RGB, HSV and CIE Lab) for parquet sorting. The results show that simple and compact colour descriptors, such as the mean of each colour channel, are as accurate as more complicated features. Likewise, we found no statistically significant difference in the accuracy attainable through the colour spaces considered in the paper. Our experiments also show that most methods are fast enough for real-time processing. The results suggest the use of simple statistical descriptors along with RGB data as the best practice to approach the problem.
Source:
F. Bianconi, A. Fernández, E. González and S.A. Saetta, "Performance analysis of colour descriptors for parquet sorting", Expert Systems With Applications, 40(5):1636-1644, 2013
Source:
F. Bianconi, A. Fernández, E. González and S.A. Saetta, "Performance analysis of colour descriptors for parquet sorting", Expert Systems With Applications, 40(5):1636-1644, 2013
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Tuesday, 04 March 2014 16:15
GEOEYE1-WV2 project just finished
'GEOEYE1-WV2: generation of high resolution geo-referenced data from GeoEye-1 and WorldView-2 satellite images' (ref. CTM2010-16573) has finalised. The project, in which Dr. Francesco Bianconi participated as a member of the research group, was coordinated by the University of Almería, Spain. Find out more.
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