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COMPUTER VISION METHOD AND SYSTEM FOR BLOB-BASED ANALYSIS USING A PROBABILISTIC FRAMEWORK

机译:基于概率框架的基于BLOB分析的计算机视觉方法和系统

摘要

Generally, techniques for analyzing foreground-segmented images are disclosed. The techniques allow clusters to be determined from the foreground-segmented images. New clusters may be added, old clusters removed, and current clusters tracked. A probabilistic framework is used for the analysis of the present invention. A method is disclosed that estimates cluster parameters for one or more clusters determined from an image comprising segmented areas, and evaluates the cluster or clusters in order to determine whether to modify the cluster or clusters. These steps are generally performed until one or more convergence criteria are met. Additionally, clusters can be added, removed, or split during this process. In another aspect of the invention, clusters are tracked during a series of images, and predictions of cluster movements are made.
机译:通常,公开了用于分析前景分割图像的技术。该技术允许从前景分割的图像确定聚类。可以添加新群集,删除旧群集,并跟踪当前群集。概率框架用于本发明的分析。公开了一种方法,该方法估计从包括分割区域的图像确定的一个或多个聚类的聚类参数,并评估一个或多个聚类以确定是否修改一个或多个聚类。通常执行这些步骤,直到满足一个或多个收敛标准为止。此外,可以在此过程中添加,删除或拆分群集。在本发明的另一方面,在一系列图像期间跟踪聚类,并对聚类运动进行预测。

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