Joint Optimization of Coded Illumination and Grayscale Conversion for One-Shot Raw Material Classification

Chao Wang and Takahiro Okabe

Abstract

Classifying materials and their surface states is important for machine vision applications such as visual inspection. In this paper, we propose an approach to one-shot per-pixel classification of raw materials on the basis of spectral BRDFs; a surface of interest is illuminated by multispectral and multidirectional light sources at the same time. Specifically, we achieve two-class classification from a single color image; it directly finds the linear discriminant hyperplane with the maximal margin in the spectral BRDF feature space by jointly optimizing the non-negative coded illumination and the grayscale conversion. In addition, we extend our method to multiclass classification by exploiting the degree of freedom of the grayscale conversion.

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DOI

10.5244/C.31.136
https://dx.doi.org/10.5244/C.31.136

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Bibtex

            @inproceedings{BMVC2017_136,
                title={Joint Optimization of Coded Illumination and Grayscale Conversion for One-Shot Raw Material Classification},
                author={Chao Wang and Takahiro Okabe},
                year={2017},
                month={September},
                pages={136.1-136.12},
                articleno={136},
                numpages={12},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.136},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.136}
            }