Abstract
In this paper, we present a new model applied to watershed segmentation optimization. Watershed segmentation is widely used in the domain of computer vision. However, in some cases, there are some unobvious gradient changing between different regions or obvious gradient changing in the same region. So the images are divided into a number of regions either smaller (under-segmentation) or larger (over-segmentation) than the expected numbers of regions. We use a new filter (OLS operator) to pre-process the image. The new filter can smooth the image but keep the edge between different regions sharper based on image decompositions. Compared to traditional watershed segmentation, our method can segment images more accurately. Experiments using an image database show the superiority of our method. Even if we apply our method to complex images with more regions, it is still effective. Furthermore, our model can be applied to other domains of computer vision (e.g., image classification, image recognition, image matting).
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