Publications:Increasing colour image segmentation accuracy by means of fuzzy post-processing
From ISLAB/CAISR
| Title | Increasing colour image segmentation accuracy by means of fuzzy post-processing |
|---|---|
| Author | Antanas Verikas and Kerstin Malmqvist |
| Year | 1995 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 1995 IEEE International Conference on Neural Networks : Proceedings, the University of Western Australia, Perth, Western Australia, 27 November-1 December 1995 (Vol. 4) |
| Conference | 1995 IEEE International Conference on Neural Networks, the University of Western Australia, Perth, Western Australia, 27 November-1 December 1995 |
| DOI | http://dx.doi.org/10.1109/ICNN.1995.488878 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:544660 |
| Abstract | This paper presents a colour image segmentation method which attains a high segmentation accuracy even when regions of the image that have to be separated are very similar in colour. The proposed method classifies pixels into colour classes. Competitive learning with `conscience' is used to learn reference patterns for the different colour classes. A nearest neighbour classification rule followed by a block of fuzzy post-processing attains a high classification accuracy even for very similar colour classes. A correct classification rate of 97.8% has been achieved when classifying two very similar black colours, namely, the black printed with a black ink and the black printed with a mixture of cyan, magenta and yellow inks. |
