Khawaja, Khalid W., authorBouman, Charles Addison, authorTretter, Daniel, authorMaciejewski, Anthony A., authorIEEE, publisher2007-01-032007-01-031995Tretter, Daniel, et al., A Multiscale Stochastic Image Model for Automated Inspection, IEEE Transactions on Image Processing 4, no. 12 (December 1995): 1641-1654.http://hdl.handle.net/10217/618In 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.born digitalarticleseng©1995 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.image processingautomatic optical inspectionBayes methodsmaximum likelihood estimationsearch problemssequential estimationstochastic processeswavelet transformsText