A new possibilistic clustering algorithm for line detection in real world imagery
✍ Scribed by Mauro Barni; Rossana Gualtieri
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 664 KB
- Volume
- 32
- Category
- Article
- ISSN
- 0031-3203
No coin nor oath required. For personal study only.
✦ Synopsis
Though promising in nature, line detection algorithms based on fuzzy clustering su!er from excessive sensitivity to noise and non-linear structures. A new detection scheme is proposed here which is suitable for the processing of real-world images. Possibilistic clustering is used instead of fuzzy clustering to achieve a higher immunity to noise, whereas a set of criteria to eliminate non-linear clusters is provided to take into account the presence of curved lines. Merging of segments is possible due to a fuzzy reasoning module exploiting human perception considerations. The number of parameters to be set is kept to a minimum, thus ensuring generality and robustness. Tests con"rm the ability of the proposed system in interpreting the linear structures present in the image.