提出新方法精准分割复杂边界图像，自动避免曲线自交问题。
A Topology fixated Shape Gradient Framework for Non Simple Boundary Extraction for CIE Lab color images with Repulsive Energy
- 用非局部能量驱动离散曲线演化，结合排斥函数处理多边界
- 可控制分割拓扑结构，有效处理嵌套与重叠区域
- 适合天文图像等复杂场景，尤其擅长避免边界自相交
本文提出一种无水平集的混合图像分割方法，基于改进的Mumford-Shah形状泛函分段常数形状梯度与排斥函数。通过非局部形状能量驱动离散曲线演化，实现对包含分离区域和多重边界的图像进行分割。该方法引入一个多变量函数，依赖曲线上少量采样点，以应对边界演化过程中的自相交问题。实验涵盖灰度图与彩色图像（包括具有嵌套结构和天体物体的图像），结果表明该方法在复杂场景中实现有效分割，能对分割区域的拓扑结构及边界自交现象实现绝对控制。
原文摘要 · Abstract (English)
A levelset free but a hybrid image segmentation approach based on a modified version of the piece wise constant shape gradient of an Mumford Shah shape functional and a repulsive function is considered. The segmentation is performed a non-local shape based through an evolution of discrete curves driven by a non local shape based energy to segment images containing disjoint regions and multiple boundaries. This formulation has a novel additional component as a multivariable function dependent on a few sampled points of the curves that handles the occurrence of self intersection during boundary curves evolution. The method is applied to a few gray scale and color images, including images with nested structures and astronomical objects. The results indicate effective segmentation in complex scenarios with absolute control on the topology of the segments and self-intersections of the boundaries
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