Research Of Image Segmentation Based On Improved Fast Watershed Algorithm

Image segmentation is an important part in image processing. Traditional methods of image segmentation can be partitioned into three main types: thresholding, edge detection, region segmentation and so on. More and more new techniques and special theories have been applied to improve image segmentation recently. The purposes of improving these methods are: higher accurate on edge position, faster and broader applications.The watershed transform algorithm raised by Vincent and Sollie, briefly named as V-S algorithm, have received wild attention for its easy principle, efficient algorithm and continuous and integrated border lines. There are three kinds of improving methods of watershed algorithm: using filters to preprocess, marking before watershed transform and region merging after watershed transform.Fast watershed algorithm has the same principle of V-S algorithm, but the former is obviously faster than the latter, because the former improves the second step of V-S algorithm. Four following improvements on fast watershed algorithm were made:(1) Concluded three cases of correcting of error minimum areas, which made the process of correcting clearer.(2) Immersion process was based on eight neighborhood, not on four neighborhood any more, which could segment more accurately and region number was smaller.(3) Former immersion was based on 1 layer, and the improved immersion process was based on multilayer, such as 5 layers and 10 layers. Compared three kinds of immersions, multilayer immersion could efficiently relieve the phenomenon of over-segmentation.(4) Region merging: combining mean chromaticity information and edge both sides mean gradient information to calculate Regional similarity.Finally, based on the improved fast watershed algorithm, an efficient, real-time image-segmentation system was designed, which can achieve good segmentation results on different types of images.