Loog Marco, de Bruijne Marleen
Pattern Recognition Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands.
Inf Process Med Imaging. 2009;21:459-66. doi: 10.1007/978-3-642-02498-6_38.
The alignment of shape data to a common mean before its subsequent processing is an ubiquitous step within the area shape analysis. Current approaches to shape analysis or, as more specifically considered in this work, shape classification perform the alignment in a fully unsupervised way, not taking into account that eventually the shapes are to be assigned to two or more different classes. This work introduces a discriminative variation to well-known Procrustes alignment and demonstrates its benefit over this classical method in shape classification tasks. The focus is on two-dimensional shapes from a two-class recognition problem.
在后续处理之前将形状数据对齐到一个共同的均值，这是区域形状分析中一个普遍存在的步骤。当前的形状分析方法，或者更具体地说，在本工作中所考虑的形状分类方法，是以完全无监督的方式进行对齐的，没有考虑到最终形状要被分配到两个或更多不同的类别中。这项工作引入了一种对著名的普罗克拉斯提斯对齐方法的判别式变体，并在形状分类任务中证明了它相对于这种经典方法的优势。重点是来自两类识别问题的二维形状。
