Publications:Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing
Property "Publisher" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user.
| Title | Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing |
|---|---|
| Author | |
| Year | 2010 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 2010 IEEE International Conference on Fuzzy Systems (FUZZ) |
| Conference | WCCI 2010 IEEE World Congress on Computational Intelligence, FUZZ-IEEE |
| DOI | http://dx.doi.org/10.1109/FUZZY.2010.5584433 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:350120 |
| Abstract | Nowadays in printing industry most of information processing steps are highly automated, except the last one–print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images. |
