Bayesian object identification
Bayesian object identification
复制标题
贝叶斯对象识别
DOI：
10.1093/biomet/86.3.649
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发表时间：
1999
期刊：
影响因子：
2.7
通讯作者：
M. Hurn
中科院分区：
文献类型：
--
作者：
H. Rue;M. Hurn
本文讨论了定位和识别图像中未知数量的不同类型的对象的任务。Baddeley &货车Lieshout（1993）主张将标记点过程作为对象先验，而Grenander &米勒（1994）使用可变形模板模型。在本文中，这两种方法的元素相结合，以处理包含不同类型的对象的可变数量的场景，使用可逆跳马尔可夫链蒙特卡罗方法进行推理（绿色，1995年）。这些方法的天真应用导致混合缓慢，我们调整模型和算法，提出三种策略来处理这个问题。前两个通过引入额外的“未知”对象类型和可变分辨率模板的思想来扩展模型空间。第三个策略，利用前两个，增强算法的更新类，提供直观的过渡之间的实现包含不同数量的细胞分裂或合并附近的对象。
This paper addresses the task of locating and identifying an unknown number of objects of different types in an image. Baddeley & Van Lieshout (1993) advocate marked point processes as object priors, whereas Grenander & Miller (1994) use deformable template models. In this paper elements of both approaches are combined to handle scenes containing variable numbers of objects of different types, using reversible jump Markov chain Monte Carlo methods for inference (Green, 1995). The naive application of these methods here leads to slow mixing and we adapt the model and algorithm in tandem in proposing three strategies to deal with this. The first two expand the model space by introducing an additional 'unknown' object type and the idea of a variable resolution template. The third strategy, utilising the first two, augments the algorithm with classes of updates which provide intuitive transitions between realisations containing different numbers of cells by splitting or merging nearby objects.