Tamminen Toni, Lampinen Jouko
Laboratory of Computational Engineering, Helsinki University of Technology, PO Box 9203, FIN-02015 HUT, Finland.
IEEE Trans Pattern Anal Mach Intell. 2006 Jun;28(6):930-41. doi: 10.1109/TPAMI.2006.128.
We consider the problem of locating instances of a known object in a novel scene by matching the fiducial features of the object. The appearance of the features and the shape of the object are modeled separately and combined in a Bayesian framework. In this paper, we present a novel matching scheme based on Sequential Monte Carlo, in which the features are matched sequentially, utilizing the information about the locations of previously matched features to constrain the task. The particle representation of hypotheses about the object position allow matching in multimodal and cluttered environments, where batch algorithms may have convergence difficulties. The proposed method requires no initialization or predetermined matching order, as the sequence can be started from any feature. We also utilize a Bayesian model to deal with features that are not detected due to occlusions or abnormal appearance. In our experiments, the proposed matching system shows promising results, with performance equal to batch approaches when the target distribution is unimodal, while surpassing traditional methods under multimodal conditions. Using the occlusion model, the object can be localized from only a few visible features, with the nonvisible parts predicted from the conditional prior model.
我们考虑通过匹配已知物体的基准特征，在新场景中定位该物体实例的问题。分别对特征的外观和物体的形状进行建模，并在贝叶斯框架中将它们结合起来。在本文中，我们提出了一种基于序贯蒙特卡罗的新颖匹配方案，其中特征是顺序匹配的，利用先前匹配特征的位置信息来约束任务。关于物体位置的假设的粒子表示允许在多模态和杂乱环境中进行匹配，而批量算法在这种环境中可能存在收敛困难。所提出的方法不需要初始化或预定的匹配顺序，因为序列可以从任何特征开始。我们还利用贝叶斯模型来处理由于遮挡或异常外观而未检测到的特征。在我们的实验中，所提出的匹配系统显示出有希望的结果，当目标分布是单峰时，其性能与批量方法相当，而在多模态条件下超过传统方法。使用遮挡模型，仅从少数可见特征就可以定位物体，不可见部分则根据条件先验模型进行预测。
