Hewlett-Packard Co., Palo Alto, CA.
IEEE Trans Image Process. 1995;4(12):1641-54. doi: 10.1109/83.475514.
We develop a novel multiscale stochastic image model to describe the appearance of a complex three-dimensional object in a two-dimensional monochrome image. This formal image model is used in conjunction with Bayesian estimation techniques to perform automated inspection. The model is based on a stochastic tree structure in which each node is an important subassembly of the three-dimensional object. The data associated with each node or subassembly is modeled in a wavelet domain. We use a fast multiscale search technique to compute the sequential MAP (SMAP) estimate of the unknown position, scale factor, and 2-D rotation for each subassembly. The search is carried out in a manner similar to a sequential likelihood ratio test, where the process advances in scale rather than time. The results of this search determine whether or not the object passes inspection. A similar search is used in conjunction with the EM algorithm to estimate the model parameters for a given object from a set of training images. The performance of the algorithm is demonstrated on two different real assemblies.
我们开发了一种新颖的多尺度随机图像模型，用于描述二维单色图像中复杂三维物体的外观。这种正式的图像模型与贝叶斯估计技术结合使用，以执行自动化检查。该模型基于随机树结构，其中每个节点是三维物体的重要组件。与每个节点或子组件相关联的数据在小波域中进行建模。我们使用快速多尺度搜索技术来计算每个子组件的未知位置、比例因子和 2-D 旋转的顺序最大后验（SMAP）估计值。搜索以类似于顺序似然比检验的方式进行，其中该过程按比例而不是按时间推进。搜索的结果决定了物体是否通过检查。类似的搜索与 EM 算法结合使用，用于从一组训练图像中估计给定物体的模型参数。该算法的性能在两个不同的真实组件上进行了演示。
