Shape reconstruction from noisy surface slope information using a multiresolution Bayesian method

Xuemei Zhang; Ramakrishna Kakarala; Zachi Izhak Baharav

doi:10.1117/12.589471

24 February 2005 Shape reconstruction from noisy surface slope information using a multiresolution Bayesian method

Xuemei Zhang, Ramakrishna Kakarala, Zachi Izhak Baharav

Proceedings Volume 5679, Machine Vision Applications in Industrial Inspection XIII; (2005) https://doi.org/10.1117/12.589471
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

Many optical inspection systems today can capture surface slope information directly or indirectly. For these systems, it is possible to perform a 3-D surface reconstruction which converts surface slopes to surface heights. Since the slope information obtained in such systems tend to be noisy and sometimes heavily quantized, a noise-tolerant reconstruction method is needed. We used a simple bayes reconstruction method to improve noise tolerance, and multi-resolution processing to improve the speed of calculations. For each resolution level, the surface slopes between pixels are first calculated from the original surface slopes. Then the height reconstruction for this resolution level is calculated by solving the linear equations that relate relative heights of each point and its related surface slopes. This is done through a Bayesian method which makes it easier to incorporate prior knowledge about height ranges and noise levels. The reconstructions are done for a small window of pixels at a time for each resolution level to make the linear equations manageable. The relative height solutions from all resolution levels are then combined to generate the final height map. This method has been used in optical inspection applications where slope data are quite noisy.

Citation Download Citation

Xuemei Zhang, Ramakrishna Kakarala, and Zachi Izhak Baharav "Shape reconstruction from noisy surface slope information using a multiresolution Bayesian method", Proc. SPIE 5679, Machine Vision Applications in Industrial Inspection XIII, (24 February 2005); https://doi.org/10.1117/12.589471

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KEYWORDS

Wavelets

Inspection

Optical inspection

Dysprosium

Image resolution

3D vision

Convolution

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