物体轮廓形状超像素图割快速提取方法∗

摘要

A rapid algorithm based on level set framework is presented for extracting object contour shapes. Firstly, initial seeds are placed in an image plane evenly. Through setting superpixel evolution forces, superpixels with similar region features are generated. The image segmented by these superpixels maintains geometric characteristics of object contour shapes and in the meantime prevents overlap between superpixel regions. Secondly, based on the relationship of superpixel labeling and Heaviside function, optimization model of the Mumford-Shah energy function is built by using graph cuts. Finally, geometric shapes of the object contour can be extracted by superpixel graph cuts. Experimental results show that the number of superpixels is reduced greatly, converted optimization model satisfies requirements of graph cuts against energy function optimization, and min-cut/max-flow method does not need to solve differential equations. Higher extracting effectiveness of object contour shapes and extracting efficiency are ensured by all these measures.%提出一种水平集框架下物体轮廓形状超像素图割快速提取方法。该方法首先均匀化放置种子点，通过对超像素化演化力的设定，生成具有区域相似特征的超像素，这些超像素对原图像的划分既能保持目标轮廓形状的几何特性，又可避免超像素间的互相重叠。然后构建超像素标号和Heaviside函数的关联关系，应用图割建立M-S能量函数的优化模型。最终利用超像素图割提取目标轮廓的几何形状。实验表明，超像素化的图像像素数目大幅度减少，转化后的优化模型符合图割对能量函数进行优化的要求，图割中最小割/最大流方法避开微分方程的求解，这些措施在保证轮廓形状提取效果的基础上提高提取效率。