A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation
A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation
复制标题
具有语义形状约束的对象分割混合水平集
DOI：
10.1109/tcyb.2018.2799999
复制
发表时间：
2019-05-01
影响因子：
11.8
通讯作者：
Tao, Dacheng
中科院分区：
文献类型：
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作者：
Wang, Bin;Yuan, Xiuying;Tao, Dacheng
提出了一种用于目标分割的混合水平集方法。该方法将分割任务分解为形状变换和曲线演化两个过程，交替优化直至收敛。在该框架中，仅利用形状上下文编码的一个形状先验来估计转换，使曲线具有与形状先验相同的语义表达，曲线演化由具有拓扑保持和核化项的能量泛函驱动。该方法具有以下优点：1)混合范式使得水平集框架具有融合形状描述子和距离等其他形状相关技术的能力；2)形状上下文赋予单个先验语义，从而导致与多个形状先验相比的竞争性能；3)此外，将拓扑保持和核化机制结合起来，有助于实现对纹理和噪声图像更合理的分割。据我们所知，我们首次提出了一种混合水平集框架，并利用形状上下文来指导曲线演化。我们的方法使用合成、医疗保健和自然图像进行了评估，因此，与同类图像相比，它显示出具有竞争力甚至更好的性能。
This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.