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

Though promising in nature, linedetectionalgorithms based on fuzzy clustering suffer from excessive sensitivity to noise and non-linear structures. Anewdetection scheme is proposed here which is suitable for the processing of real-world images. Possibilisticclustering 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.