基于邻域结构和高斯混合模型的非刚性点集配准算法

彭磊; 李光耀; 肖莽; 王刚; 谢力

doi:10.11999/JEIT150501

基于邻域结构和高斯混合模型的非刚性点集配准算法

doi: 10.11999/JEIT150501

彭磊¹,
李光耀²,
肖莽²,
王刚²,
谢力²

2.
(同济大学电子与信息工程学院上海 201804) ②(泰山医学院信息工程学院泰安 271016)

基金项目:

山东省自然科学基金(ZR2015FL005)，泰安市科技发展计划(2015GX2016)

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- 文章访问数: 1857
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- 被引次数: 0
出版历程
- 收稿日期: 2015-04-30
- 修回日期: 2015-10-08
- 刊出日期: 2016-01-19

Non-rigid Point Set Registration Based on Neighbor Structure and Gaussian Mixture Models

2.
(College of Electronic Information and Engineering, Tongji University, Shanghai 201804, China)

Funds:

Shandong Provincial Natural Science Foundation, China (ZR2015FL005), Taian Science and Technology Development Program, China (2015GX2016)

摘要

摘要: 非刚性点集配准算法在实际应用中要求对噪声、遮挡或异常点具有很好的鲁棒性。该文采用高斯混合模型并结合点的邻域结构信息实现非刚性点集配准。使用高斯混合模型表示模型点集，通过高斯径向基函数构建变换模型。并根据点的邻域结构信息决定高斯混合模型中每个高斯组成部分所占的比例。在EM算法的期望步(E-step)阶段求解点的对应关系，在最大化步(M-step)阶段求解异常点比例系数和变换的闭合形式解，直至算法收敛得到最优解。通过在合成数据和实际的视网膜图像上的实验，与目前几种先进的点集配准方法进行了比较，证明该算法具有较好的配准效果和鲁棒性。
- 图像配准 /
- 非刚性点集配准 /
- 高斯混合模型 /
- 结构描述符
Abstract: In the practical application, non-rigid point set registration should be robust for noise, occlusion or outliers. In this paper, Gaussian Mixture Model (GMM) and neighborhood structure information are used for the non-rigid point set registration. Gaussian Mixture Model is used to represent the model set, and the transformation is built by using Gaussian radial basis function. The proportion of each Gaussian component is decided by the neighborhood structure information of points. In E-step of the EM algorithm the correspondence is solved, and in M-step the outlier ratio and the closed-form solution of the transformation are calculated. Until convergence the optimal solution is obtained. As compared to the state-of-the-art algorithms, the experiments with synthetic data and real data of the retina images show that the proposed method can improve the robustness and the accuracy.
- Image registration /
- Non-rigid point set registration /
- Gaussian mixture model /
- Structure descriptor

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