A new possibilistic clustering algorithm for line detection in real world imagery

摘要

Though promising in nature, line detection algorithms based on fuzzy clustering suffer 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 confirm the ability of the proposed system in interpreting the linear structures present in the image.