Accurate, fast, and robust centre localisation for images of semiconductor components

Fabian Timm; Erhardt Barth

doi:10.1117/12.872386

7 February 2011 Accurate, fast, and robust centre localisation for images of semiconductor components

Fabian Timm, Erhardt Barth

Proceedings Volume 7877, Image Processing: Machine Vision Applications IV; 787705 (2011) https://doi.org/10.1117/12.872386
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

The problem of circular object detection and localisation arises quite often in machine vision applications, for example in semi-conductor component inspection. We propose two novel approaches for the precise centre localisation of circular objects, e.g. p-electrodes of light-emitting diodes. The first approach is based on image gradients, for which we provide an objective function that is solely based on dot products and can be maximised by gradient ascend. The second approach is inspired by the concept of isophotes, for which we derive an objective function that is based on the definition of radial symmetry. We evaluate our algorithms on synthetic images with several kinds of noise and on images of semiconductor components and we show that they perform better and are faster than state of the art approaches such as the Hough transform. The radial symmetry approach proved to be the most robust one, especially for low contrast images and strong noise with a mean error of 0.86 pixel for synthetic images and 0.98 pixel for real world images. The gradient approach yields more accurate results for almost all images (mean error of 4 pixel) compared to the Hough transform (8 pixel). Concerning runtime, the gradient-based approach significantly outperforms the other approaches being 5 times faster than the Hough transform; the radial symmetry approach is 12% faster.

Citation Download Citation

Fabian Timm and Erhardt Barth "Accurate, fast, and robust centre localisation for images of semiconductor components", Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 787705 (7 February 2011); https://doi.org/10.1117/12.872386

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