Retroreflective microprismatic materials in image-based control applications

Mariya G. Serikova; Anton V. Pantyushin; Elena V. Gorbunova; Andrei G. Anisimov

doi:10.1117/12.2184831

Abstract

This work addresses accurate position measurement of reference marks made of retroreective microprismatic materials by image-based systems. High reflection microprismatic technology implies tiny hermetically sealed pockets, which improve material reflectivity, but result in non-reflective preprinted netting pattern. The mark pattern to be used for measuring can be simply printed on the reflective material as an opaque area with predefined shape. However, the non-reflecting pattern acts as a spatial filter that affects resultant spatial reflectivity of the mark. When an image of the mark is taken, the desired mark shape can be deformed by the netting pattern. This deformational may prevent accurate estimation of the mark position in the image. In this paper experimental comparison of three image filtering approaches (median filtering, morphological close and filtering in a frequency domain) in order to minimize the affection of the netting pattern is provided. These filtering approaches were experimentally evaluated by processing of the images of the mark that was translated in a camera field of view. For that a developed experimental setup including a camera with LED backlight and the mark placed on a translation stage was used. The experiment showed that median filtering provided better netting pattern elimination and higher accuracy of key features position estimation (approximately ±0.1 pix) in the condition of the experiment. The ways of future use of reference marks based on microprismatic material in image-based control applications are discussed.

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Mariya G. Serikova, Mariya G. Serikova, Anton V. Pantyushin, Anton V. Pantyushin, Elena V. Gorbunova, Elena V. Gorbunova, Andrei G. Anisimov, Andrei G. Anisimov, } "Retroreflective microprismatic materials in image-based control applications", Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300E (22 June 2015); doi: 10.1117/12.2184831; https://doi.org/10.1117/12.2184831

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