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
Journal Title
Journal ISSN
Volume Title
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.
Rights Access
image processing
automatic optical inspection
Bayes methods
maximum likelihood estimation
search problems
sequential estimation
stochastic processes
wavelet transforms
Associated Publications