title: A multiscale stochastic image model for automated inspection.
abstract: 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.
authors.affiliation: Hewlett-Packard Co., Palo Alto, CA.
authors.forename: D
authors.initials: D
authors.lastname: Tretter
authors.forename: C A
authors.initials: CA
authors.lastname: Bouman
authors.forename: K W
authors.initials: KW
authors.lastname: Khawaja
authors.forename: A A
authors.initials: AA
authors.lastname: Maciejewski