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Rule-based soft computing for edge detection

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Abstract

In this paper, we present a robust rule-based edge detection method. Although generalized edge detection approaches are effective for most images they often fail in others. Thus the goal of our method is to provide more reliable edge detection results that are effective in most images. We implement the proposed method as follows: (1) transform RGB images to YCbCr format, (2) apply Sobel mask in four edge directions (horizontal, vertical, diagonal, anti-diagonal), (3) apply a bi-directional mask in four edge directions (horizontal–diagonal, vertical–diagonal, horizontal–anti-diagonal, vertical–anti-diagonal), and (4) detect rule-based edges by calculating membership degrees. Simulation results demonstrate that the proposed method is effective in most given images. We used three benchmarks approaches (Canny edge mask, high-pass filter, and Sobel mask) to compare the subjective performance quality.

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Notes

  1. We note that the intensity range [0,1] is identical to [0,255].

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Acknowledgements

This authors acknowledge the financial support of Converging Research Program (CRP-20141311) through Incheon National University, Republic of Korea.

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Authors and Affiliations

  1. Department of Embedded Systems Engineering, College of Information and Technology, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Korea

    Byoungjo Choi, Seokhoon Kang, Kyungkoo Jun & Joonghwee Cho

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  1. Byoungjo Choi
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  2. Seokhoon Kang
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  3. Kyungkoo Jun
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  4. Joonghwee Cho
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Correspondence to Kyungkoo Jun.

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Choi, B., Kang, S., Jun, K. et al. Rule-based soft computing for edge detection. Multimed Tools Appl 76, 24819–24831 (2017). https://doi.org/10.1007/s11042-016-4329-7

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