Dipartimento d'Ingegneria

Bianconi Francesco

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 Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Additional Details

  • Phone
    075 585 3706
  • Role
    Professore associato - Associate professor
  • Area
    Progettazione industriale, costruzioni meccaniche e metallurgia - Engineering design and metallurgy
  • Curriculum Vitae
Friday, 02 May 2014 17:30

Texture databases for benchmarking

Texture analysis is an area of intense research activity. Like in other fields, the availability of public data for benchmarking is vital to the development of the discipline. In ‘‘Texture databases – A comprehensive survey’’, Hossain and Serikawa recently provided a precious review of a good number of texture datasets. We have recently proposed an appendix to complement the cited work by providing reference to additional image databases of bio-medical textures, textures of materials and natural textures that have been recently employed in experiments with texture analysis.

Source:
F Bianconi and A. Fernández, An appendix to ‘‘Texture databases – A comprehensive survey’’, Pattern Recognition Letters, 45(1):33-38,2014
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
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.

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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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