A multiscale stochastic image model for automated inspection

A multiscale stochastic image model for automated inspection

Tretter, Daniel ; Bouman, Charles Addison ; Khawaja, Khalid W. ; Maciejewski, Anthony A.

"This work was supported by an AT&T Bell Laboratories Ph.D. Scholarship, the NEC corporation, National Science Foundation grant number MIP93-00560, and National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems."

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.

Colorado State University. Libraries

1995

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ECEaam00020.pdf

FACFECEN100020ARTI

eng

English

c1995, IEEE