In this paper, we present an efficient and robust algorithm for shape matching, registration, and detection. The task is to geometrically transform a source shape to fit a target shape. The measure of similarity is defined in terms of the amount of transformation required. The shapes are represented by sparse-point or continuous-contour representations depending on the form of the data. We formulate the problem as probabilistic inference using a generative model and the EM algorithm. But this algorithm has problems with initialization and computing the E-step. To address these problems, we define a discriminative model which makes use of shape features. This gives a hybrid algorithm which combines the generative and discriminative models. The resulting algorithm is very fast, due to the effectiveness of shape-features for solving correspondence requiring typically only four iterations. The convergence time of the algorithm is under a second. We demonstrate the effectiveness of the algorithm by testing it on standard datasets, such as MPEG7, for shape matching and by applying it to a range of matching, registration, and foreground/background segmentation problems.

中文翻译：

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通过数据驱动的 EM 进行形状匹配和配准。


在本文中，我们提出了一种用于形状匹配、配准和检测的高效且鲁棒的算法。任务是对源形状进行几何变换以适合目标形状。相似性的度量是根据所需的转换量来定义的。根据数据的形式，形状由稀疏点或连续轮廓表示形式表示。我们使用生成模型和 EM 算法将该问题表述为概率推理。但该算法在初始化和计算E步方面存在问题。为了解决这些问题，我们定义了一个利用形状特征的判别模型。这给出了一种结合了生成模型和判别模型的混合算法。由于形状特征对于解决对应关系的有效性通常只需要四次迭代，因此所得算法非常快。该算法的收敛时间小于一秒。我们通过在标准数据集（例如 MPEG7）上测试该算法的形状匹配并将其应用于一系列匹配、配准和前景/背景分割问题来证明该算法的有效性。