A multiscale stochastic image model for automated inspection
Date
1995
Authors
Khawaja, Khalid W., author
Bouman, Charles Addison, author
Tretter, Daniel, author
Maciejewski, Anthony A., author
IEEE, publisher
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Volume Title
Abstract
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.
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Subject
image processing
automatic optical inspection
Bayes methods
maximum likelihood estimation
search problems
sequential estimation
stochastic processes
wavelet transforms