A multiscale stochastic image model for automated inspection - Khawaja, Khalid W., author; Bouman, Charles Addison, author; Tretter, Daniel, author; Maciejewski, Anthony A., author; IEEE, publisher (Colorado State University. Libraries, 1995)
In this paper, 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.
Keywords: image processing; automatic optical inspection; Bayes methods; maximum likelihood estimation; search problems; sequential estimation; stochastic processes; wavelet transforms