Determining the maximal inscribed rectangle of an irregularly shaped stone using machine vision

ABSTRACT

This study uses machine vision approaches to develop a rotation histogram method with better area utilization and shorter execution time that automatically determines the maximal inscribed rectangle of an irregularly shaped stone. This image processing procedure is designed to enhance the stone image, reduce the contour noise, determine the stone contour and the calculation range of the computational algorithm, and speed up the determination of the maximal inscribed rectangle of irregularly shaped stones. The rotation histogram method transforms to a histogram the stone image after-image processing, and then uses the stack method to determine the maximal rectangle of the histogram. Finally, the stone image is rotated to determine the maximal inscribed rectangle and angle of the irregularly shaped stone. The rotation histogram method is applicable to irregularly shaped stones with internal voids, and the area utilization of the stone will not be influenced by the placement orientation. The experimental results show that the rotation histogram method, as designed in this study, not only increases the area utilization for achieving the average value of 3.27%, but it also significantly shortens the execution time to achieve an average value of 64.83% compared with the existing traversal center diffusion method.

KEYWORDS:

• Maximal inscribed rectangle
• irregularly shaped stone
• machine vision

Acknowledgments

This research was supported in part by the Ministry of Science and Technology, Taiwan, under Contract MOST108-2221-E-027-108 and MOST108-2218-E-009-032. The authors would like to thank Dr. Po-Hung Wu (Stone and Resource Industry R&D Center, Taiwan) for his valuable comments on stone cutting and manufacturing processes.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST108-2218-E-009-032, MOST108-2221-E-027-108].