基于SLIC与分水岭算法的彩色图像分割

为了克服传统分水岭算法引起的过分割问题，提出了一种基于简单线性迭代聚类(SLIC)与分水岭算法相结合的彩色图像分割算法，以获得更理想的分割效果。该算法首先利用图像复杂度计算预分割的超像素个数，并利用SLIC对原始图像进行超像素分割预处理，以减少后续处理中的冗余信息；然后，提出了一种自适应计算阈值的方法对预处理图像的梯度图像进行阈值处理，以有效去除噪声，获得较完整的轮廓信息；最后，利用分水岭分割算法对进行极小值标记提取后的图像进行分割。通过对大量图片进行实验表明，本文算法可以有效地抑制传统分水岭算法所产生的过分割问题，在LCE和GCE的对比上优于传统算法，分割质量有所提高。

Color image segmentation based on SLIC and watershed algorithm

In order to overcome the problem of over-segmentation caused by traditional watershed algorithm, a color image segmentation algorithm based on simple linear iterative clustering (SLIC) and watershed algorithm is proposed to achieve an ideal segmentation effect. Firstly, the algorithm calculates the number of super-pixels pre-segmented by image complexity, and uses SLIC to super-pixel segmentation preprocessing of the original image to reduce the redundant information in subsequent processing. Then, an adaptive threshold calculation method is proposed to process the gradient image of the preprocessed image in order to effectively remove noise and obtain more complete contour information. Finally, the watershed segmentation algorithm is used to segment the image extracted by the minimum value marker. Experiments on a large number of images show that the proposed algorithm can effectively suppress the over-segmentation problem caused by the traditional watershed algorithm, and is superior to the traditional algorithm in the comparison of LCE and GCE, and the segmentation quality is improved.