Using spherical mirrors in place of wide-angle cameras allows for cost-effective monitoring of manufacturing processes in hazardous environment, where a camera would normally not operate. This includes environments of high heat, vacuum, and strong electromagnetic fields. Moreover, it allows the layering of multiple camera types (e.g., color image, near-infrared, long-wavelength infrared, ultraviolet) into a single wide-angle output, whilst accounting for the different camera placements and lenses used. Normally, the different camera positions introduce a parallax shift between the images, but with a spherical projection as produced by a spherical mirror, this parallax shift is reduced, depending on mirror size and distance to the monitoring target. This paper introduces a variation of the ‘mirror ball projection,’ that accounts for distortion produced by a perspective camera at the pole of the projection. Finally, the efficacy of process monitoring via a mirror ball is evaluated.

1. [1] D. Floreano, S. Mitri, S. Magnenat, and L. Keller, “Evolutionary conditions for the emergence of communication in robots,” Current Biology, Vol.17, No.6, pp. 514-519, 2007. https://doi.org/10.1016/j.cub.2007.01.058
2. [2] P. Debevec, P. Graham, J. Busch, and M. Bolas, “A single-shot light probe,” ACM SIGGRAPH 2012 Talks, ser. SIGGRAPH’12, 2012. https://doi.org/10.1145/2343045.2343058
3. [3] P. D. Bourke, “Using a spherical mirror for projection into immersive environments,” Graphite (ACM Siggraph), Dunedin Nov/Dec 2005, 2005. http://paulbourke.net/papers/graphite2005/graphite.pdf [Accessed February 24, 2024]
4. [4] Z. Zivkovic and O. Booij, “How did we built our hyperbolic mirror omni-directional camera—Practical issues and basic geometry,” IAS Technical Reports, 2005. http://hep.ucsb.edu/people/hnn/n/scintillation_optics_info/a_hyperbolic_mirror_experiment_info.pdf [Accessed February 24, 2024]
5. [5] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Processing, Vol.13, No.4, pp. 600-612, 2004. https://doi.org/10.1109/TIP.2003.819861
6. [6] Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? a new look at signal fidelity measures,” IEEE Signal Processing Magazine, Vol.26, No.1, pp. 98-117, 2009. https://doi.org/10.1109/MSP.2008.930649
7. [7] C. Zauner, “Implementation and benchmarking of perceptual image hash functions,” Diplomarbeit, University of Applied Sciences Hagenberg, 2010. https://www.phash.org/docs/pubs/thesis_zauner.pdf [Accessed October 18, 2024]
8. [8] R. W. Hamming, “Error detecting and error correcting codes,” Bell System Technical J., Vol.29, No.2, pp. 147-160, 1950. https://archive.org/details/bstj29-2-147/page/n1/mode/2up
9. [9] W. Artsimovich, A. Yamashita, Y. Taniguchi, N. Koshii, Y. Nishiyama, T. Oshima, S. Nango, and D. Kono, “Enhancing Machining Operators’ Situational Awareness Through XR Visualization,” Proc. of the 15th Conf. on Production Machining and Machine Tools (MMTC), 2024 (in Japanese).