Flood Fill Mean Shift: A Robust Segmentation Algorithm

Flood Fill Mean Shift: A Robust Segmentation Algorithm

In this paper, the flood fill mean shift (FFMS) is introduced. This algorithm is developed for robust segmentation by improving the mean shift (MS) through the flood fill (FF) technique, instead of relying on spatial bandwidth. Due to this exchange, the FFMS involves only one parameter, the range bandwidth, which is not sensitive and is able to acquire global characteristics. If the image parts af-fected by the illumination changes are sufficiently small and their boundaries are not clear, the illumi-nation effects do not influence the mode seeking procedure of the proposed FFMS. To prove the use-fulness and the validity of our algorithm, we present several experiments and analysis of the results.

In this paper, the flood fill mean shift (FFMS) is introduced. This algorithm is developed for robust segmentation by improving the mean shift (MS) through the flood fill (FF) technique, instead of relying on spatial bandwidth. Due to this exchange, the FFMS involves only one parameter, the range bandwidth, which is not sensitive and is able to acquire global characteristics. If the image parts af-fected by the illumination changes are sufficiently small and their boundaries are not clear, the illumi-nation effects do not influence the mode seeking procedure of the proposed FFMS. To prove the use-fulness and the validity of our algorithm, we present several experiments and analysis of the results.