Abstract
Big rocks are an important problem in the mining industry. They could block machines and cause high costs of preservation equipment. This problem reduces the tonnage productivity and increases the machine down-time. This paper utilizes marker based watershed algorithm for detecting big rocks. First, an inverted binary of the original picture is created. Then the regional maximum distance function of this image is used to mark the rocks in the image. Finally, image fusion on decision level is used to improve rate of big rock detection. Proposed algorithm is examined with a data set that involves 40 big rocks and calculated rate of big rock detection. Result indicated multiple images fusion could decrease error in big rock detection.
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© 2007 Springer-Verlag Berlin Heidelberg
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Al Modarresi, M.T., Tabatabaei, M.S., Sadeghi, M.T. (2007). A Hybrid Big Rock Detection Algorithm Based on Multiple Images Fusion and Watershed. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_93
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DOI: https://doi.org/10.1007/978-3-540-75175-5_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
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