Abstract
Accurate segmentation of phases is an extremely crucial part of phase quantification and identification. In this paper, an efficient image segmentation process has been proposed using watershed techniques. The proposed image processing method has been applied to public datasets of ultra-high carbon steel (UHCS) scanning electron microscopy (SEM) microstructure images. The experimental results clearly show the phase segmentation of images with fine, blocky structures, and significant foreground and background differences. The detection of grains appears to be credible for images with reasonable foreground and background differences. However, detective efficiency is relatively poor in the case of images that can’t differentiate between foreground and background. In most instances of compositional range, heat treatment, and magnification, the proposed image processing technique shows promising results in UHCS SEM images.
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Jain, M., Jain, V., Choudhury, A., Ghosh, M. (2023). Phase Segmenting Process in Ultra-High Carbon Steels Using Deep Vision Approach. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_17
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DOI: https://doi.org/10.1007/978-981-19-5292-0_17
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