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
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.
We studied a sequential, two-step procedure based on machine vision for detecting and characterizing impurities in paper. The method is based on a preliminary classification step to differentiate defective paper patches (i.e.: with impurities) from non-defective ones (i.e.: with no impurities), followed by a thresholding step to separate the impurities from the background. This approach permits to avoid the artifacts that occurs when thresholding is applied to paper samples that contain no impurities. We discuss and compare different solutions and methods to implement the procedure and experimentally validate it on a datasets of 11 paper classes. The results show that a marked increase in detection accuracy can be obtained with the two-step procedure in comparison with thresholding alone.

ImagingSystem
Source:
F. Bianconi, L. Ceccarelli, A. Fernández and S. A. Saetta, "A sequential machine vision procedure for assessing paper impurities", Computers in Industry, 65(2):325-332, 2014
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