Abstract
The ultimate print quality evaluation is always based on end-users’ “quality experience”, and therefore, the main challenge in automatic evaluation is to model the visual path and cognition process from physical properties to the experience. The present efforts to automate print quality evaluation have been concentrated on automating the current manually-performed assesments, which reduces the laborious work, but does not provide any novel information. In this work, a new approach for automating the evaluation is proposed and the approach is utilised by defining new computational level features which are able to explain visual quality evaluations performed by human experts.
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References
Heeschen, W., Smith, D.: Robust digital image analysis method for counting missing dots in gravure printing. In: Proc. Int. Printing & Graphic Arts Conference, Atlanta, GA, USA, pp. 29–35 (2000)
Langinmaa, A.: An image analysis based method to evaluate gravure paper quality. In: Proc. 11th IAPR Int. Conf. on Computer Vision and Applications, pp. 777–780 (1992)
Vartiainen, J., Sadovnikov, A., Kamarainen, J.K., Lensu, L., Kalviainen, H.: Detection of irregularities in regular patterns. Machine Vision and Applications (Accepted for publication)
Fahlcrantz, C.M., Johansson, P.A.: A comparison of different print mottle evaluation models. In: 56th Annual Technical Conference of the Technical Association of the Graphic Arts, San Antonio, USA, pp. 511–525 (2004)
Sadovnikov, A., Lensu, L., Kamarainen, J., Kalviainen, H.: Quantifed and perceived unevenness of solid printed areas. In: Xth Ibero-American Congress on Pattern Recognition, pp. 710–719 (2005)
Wolin, D.: Enhanced mottle measurement. In: PICS 2002. IS&T’s PICS conference, pp. 148–151 (2002)
Levlin, J.E., Söderbjelm, L.: Pulp and Paper Testing. In: Papermaking Science and Technology. Fapet Oy (1999)
Niskanen, K.: Paper physics. In: Papermaking Science and Technology, Fapet Oy (1998) ISBN 952-5216-16-0
Oittinen, P., Saarelma, H.: Printing. In: Papermaking Science and Technology, Fapet Oy (1999)
Doyle, M.: Measuring the imperfect dot. In: IS&T’s NIP16: Int. Conf. on Digital Printing Technologies, pp. 640–642 (2000)
Oittinen, P., Saarelma, H.: Kuvatekninen Laatu. Otatieto (1992)
Kipman, Y.: Image quality metrics for printers and media. In: Proc. of the PICS Image Processing, Image Quality, Image Capture, Systems Conf., pp. 183–187 (1998)
Vartiainen, J., Paalanen, P., Kamarainen, J.K., Lensu, L., Kälviäinen, H.: Minimum error contrast enhancement. Research report 102, Department of Information Technology, Lappeenranta University of Technology (2006)
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Eerola, T., Kamarainen, JK., Lensu, L., Kälviäinen, H. (2007). Visual Print Quality Evaluation Using Computational Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76858-6_40
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DOI: https://doi.org/10.1007/978-3-540-76858-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76857-9
Online ISBN: 978-3-540-76858-6
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