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Heuristic Analysis for In-Plane Non-Contact Calibration of Rulers Using Mask R-CNN
| Content Provider | MDPI |
|---|---|
| Author | Telahun, Michael Sierra-Sossa, Daniel Elmaghraby, Adel S. |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 259 |
| e-ISSN | 20782489 |
| DOI | 10.3390/info11050259 |
| Journal | Information |
| Issue Number | 5 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-05-09 |
| Access Restriction | Open |
| Subject Keyword | Information Artificial Intelligence Image Segmentation Deep Learning Morphology Heuristic Non-contact Measure Computer Vision Image Registration |
| Content Type | Text |
| Resource Type | Article |
