Dipartimento d'Ingegneria

Monday, 05 May 2014 11:48

IMVIP 2014

The Irish Machine Vision and Image Processing conference (IMVIP 2014) will be hosted by the Intelligent Systems Research Centre (ISRC) at the University of Ulster, Magee from 27th-29th August 2014. IMVIP 2014 is the main conference of the Irish Pattern Recognition and Classification Society. The conference emphasises both theoretical research results and practical engineering experience in all areas of machine vision and image processing. Call for papers and additional information can be found at https://sites.google.com/site/imvip2014/. Please note that the deadline for submissions has been extended to Wednesday 14th May 2014.
Published in Research
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.

OAK 04 
OAK 08

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

Published in Research
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.

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
Published in Research


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