Abstract
Detection of line segments in a digital picture is viewed as a clustering problem through application of the adaptive fuzzy clustering (AFC) algorithm. For each line detected, the AFC gives the line description in terms of the end-points of the line as well as its weighted geometric center. The results of the AFC technique are compared with the results of the fuzzy c-lines (FCL) and fuzzy c-elliptotypes (FCE) algorithms and superiority of AFC is demonstrated. It is also shown that the output of the AFC algorithm is not very sensitive to the number of clusters to be searched for. A major advantage of the AFC approach is that it does not require ordered ( e.g. chain-coded ) image data-points. Thus it is comparable to the global line detection technique like Hough transforms (HT). The AFC method requires less memory than the HT method and is shown to work better for polygonal descriptions of digital curves. A variation of the AFC algorithm is introduced in order to improve the computational efficiency.
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