Dipartimento d'Ingegneria

Engineering design and metallurgy - Progettazione industriale, costruzioni meccaniche e metallurgia

The research interests of the group include all tools and methods related to any stage of product design and manufacturing.
At present the activity is mainly focussed on the following areas: machine design; system dynamics; structural mechanics; computer-aided engineering (including finite elements analysis, computational fluid dynamics and multi-body simulation); fatigue mechanics; random loads fatigue; comfort evaluation; motion sickness analysis; product design; design tools and method in Engineering; engineering drawing; computer-aided design; design for life-cycle; tolerance analysis; machine vision and machine learning for industrial applications.

Colour descriptors for parquet sorting

In Research ,
Scritto da Mercoledì, 05 Marzo 2014 10:43
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


GEOEYE1-WV2 project just finished

In Projects ,
Scritto da Martedì, 04 Marzo 2014 16:15
'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.

Machine vision in the papermaking industry

In Research ,
Scritto da Martedì, 04 Marzo 2014 16:02
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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