Efficient and accurate structured light algorithm for direct measurement of cylindrical surface parameters

The recognition of surface shapes using structured light is an important component of many industrial vision systems. The iterative correction algorithm (ICA) for the recognition of cylindrical objects is based upon a predictor-corrector approach which utilizes an initial estimate for the surface parameters, followed by iterative parameter refinement. A predicted passive image is generated using the current surface parameter estimates and significant features are extracted and compared with those in the true passive image. The estimated surface parameters are corrected based upon feature disparities. In this paper, the ICA is enhanced with a new method for computing passive-image point coordinates and tested. Future research directions are indicated.

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