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A multiscale stochastic image model for automated inspection.

D Tretter¹, C A Bouman, K W Khawaja

¹Hewlett-Packard Co., Palo Alto, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1995

Summary

We developed a new image model for automated inspection of 3D objects. This multiscale stochastic model uses Bayesian estimation and wavelet analysis for accurate subassembly analysis and quality control.

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Area of Science:

Computer Vision
Image Processing
Statistical Modeling

Background:

Automated inspection of complex 3D objects in 2D images presents significant challenges.
Existing methods may lack the precision required for detailed subassembly analysis.
Stochastic modeling offers a robust framework for handling image variability.

Purpose of the Study:

To develop a novel multiscale stochastic image model for automated inspection.
To integrate this model with Bayesian estimation for precise object analysis.
To enable accurate determination of object parameters and pass/fail status.

Main Methods:

A stochastic tree structure models the 3D object's subassemblies.
Wavelet domain modeling captures data associated with each subassembly.

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A fast multiscale search computes sequential MAP (SMAP) estimates for position, scale, and rotation.

The Expectation-Maximization (EM) algorithm estimates model parameters from training data.

Main Results:

The developed model accurately describes complex 3D object appearance in 2D images.
The SMAP estimation successfully determines subassembly parameters.
The algorithm demonstrates effective performance on real-world assemblies.
Model parameters are efficiently estimated using the EM algorithm.

Conclusions:

The novel multiscale stochastic image model provides a powerful tool for automated inspection.
Bayesian estimation and wavelet analysis enhance the accuracy of subassembly analysis.
This approach offers a robust solution for quality control in manufacturing and assembly processes.