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Heuristic Analysis for In-Plane Non-Contact Calibration of Rulers Using Mask R-CNN

Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
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Author to whom correspondence should be addressed.
Information 2020, 11(5), 259; https://doi.org/10.3390/info11050259
Received: 9 April 2020 / Revised: 25 April 2020 / Accepted: 6 May 2020 / Published: 9 May 2020
Determining an object measurement is a challenging task without having a well-defined reference. When a ruler is placed in the same plane of an object being measured it can serve as metric reference, thus a measurement system can be defined and calibrated to correlate actual dimensions with pixels contained in an image. This paper describes a system for non-contact object measurement by sensing and assessing the distinct spatial frequency of the graduations on a ruler. The approach presented leverages Deep Learning methods, specifically Mask Region proposal based Convolutional Neural Networks (R-CNN), for rulers’ recognition and segmentation, as well as several other computer vision (CV) methods such as adaptive thresholding and template matching. We developed a heuristic analytical method for calibrating an image by applying several filters to extract the spatial frequencies corresponding to the ticks on a given ruler. We propose an automated in-plane optical scaling calibration system for non-contact measurement. View Full-Text
Keywords: image segmentation; deep learning; morphology; heuristic; non-contact measure; computer vision; image registration image segmentation; deep learning; morphology; heuristic; non-contact measure; computer vision; image registration
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Telahun, M.; Sierra-Sossa, D.; Elmaghraby, A.S. Heuristic Analysis for In-Plane Non-Contact Calibration of Rulers Using Mask R-CNN. Information 2020, 11, 259.

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