Combinatorial photometric stereo and its application in 3D modeling of melanoma
Zhou, Y., Smith, M., Smith, L. and Warr, R. (2012) Combinatorial photometric stereo and its application in 3D modeling of melanoma. Machine Vision and Applications, 23 (5). pp. 1029-1045. ISSN 0932-8092 Available from: http://eprints.uwe.ac.uk/16124
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Publisher's URL: http://dx.doi.org/10.1007/s00138-011-0356-6
Abstract This article concerns a new type of photometric stereo algorithm for which outliers such as highlights and shadows, including attached and cast shadow, are mixed with Lambertian data. The underlying motivation behind this algorithm is very simple: an axial symmetrical setup in 6-light photometric stereo can be used to offer advantages. We investigate why an axial symmetrical setup is useful and how it can be used to improve standard photometric stereo. The main result is summarized as a combinatorial photometric stereo algorithm which embeds a non-Lambertian detection procedure. To apply this algorithm, it involves three steps. First, it combines a group of reflectance intensities to make five virtual images, whose equivalence is guaranteed due to the axial symmetrical setup of the 6-source photometric stereo system. Second, comparison between these virtual images generates a five by five skew-symmetric matrix. The Frobenius norm of this matrix is then employed as an index to determine whether there is a non-Lambertian pixel present among the six pixels. Finally, after identification of non-Lambertian pixels, standard photometric stereo is performed to realize 3D modeling. Validation of this algorithm has been conducted with both synthetic and real images. The real images were obtained from a newly designed 3D imaging device, the Skin Analyzer, for clinical inspection of melanoma. Experimental study shows that combinatorial photometric stereo gives promising results in suppressing shadows and highlights, while improving 3D reconstruction results. Furthermore, error analysis illustrates how to determine an appropriate threshold value to enable the algorithm to achieve optimal performance.
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