一种基于2DPCA训练的形状先验提取方法

面向噪声图像分割问题提出了一种基于二维主成分分析(two-dimensional principal component analysis,2DPCA)训练的形状先验提取方法. 首先对无噪声形状进行训练，得到一组标准正交投影方向并张成2DPCA空间. 将噪声图像投影到该空间，并在张成的空间中应用最小二乘法找到跟该投影点距离最近的点. 该点的原象未必是原来的训练形状，而可能是它们的线性组合. 最后在原来的空间中找到该原象，重构出先验形状.实验结果表明利用所得形状先验对含噪声以及含遮挡和缺失内容的图像分割具有明显效果.

Shape Prior Extraction Based on 2DPCA Training

A shape prior extraction method based on two-dimensional principal component analysis (2DPCA)training is proposed for noisy image segmentation. Some ideal shapes are trained to obtain a group orthonormal projection vectors, and are then spanned to the 2DPCA space. The noisy image is projected onto this space, and least squares method is employed to find a dot nearest the projected dot in this space. The pre-image of this dot may not be one of the training shapes, but a linear combination of them. A pre-image strategy is then exploited to extract the shape prior. Experimental results show that the proposed segmentation method with shape prior is valid not only for noisy images, but also for images with occlusion and massing parts.