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首页> 外文会议>International Conference on Pattern Recognition Applications and Methods >Fundamentals of Nonparametric Bayesian Line Detection
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Fundamentals of Nonparametric Bayesian Line Detection

机译：非参数贝叶斯线检测的基础

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Line detection is a fundamental problem in the world of computer vision. Many sophisticated methods have been proposed for performing inference over multiple lines; however, they are quite ad-hoc. Our fully Bayesian model extends a linear Bayesian regression model to an infinite mixture model and uses a Dirichlet Process as a prior. Gibbs sampling over non-unique parameters as well as over clusters is performed to fit lines of a fixed length, a variety of orientations, and a variable number of data points. Bayesian inference over data is optimal given a model and noise definition. Initial computer experiments show promising results with respect to clustering performance indicators such as the Rand Index, the Adjusted Rand Index, the Mirvin metric, and the Hubert metric. In future work, this mathematical foundation can be used to extend the algorithms to inference over multiple line segments and multiple volumetric objects.
机译：线路检测是计算机愿景世界的根本问题。已经提出了许多复杂的方法来对多条线进行推断;但是，它们是相当的ad-hoc。我们完全贝叶斯模型将线性贝叶斯回归模型扩展到无限混合模型，并使用Dirichlet方法作为先前。 GIBBS对非唯一参数以及群集进行采样，以适合固定长度，各种取向和可变数量的数据点的线。贝叶斯推断通过数据是最佳的，给定模型和噪声定义。初始计算机实验表明，关于兰特指数，调整后的兰特指数，Mirvin指标和Hubert指标等群体绩效指标表明了有希望的结果。在未来的工作中，该数学基础可用于扩展算法以推断多线段和多个体积对象。

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