Research of Image Segmentation Based on Iterative Threshold

Lei Shao; Yi Mu; Peng Guo; Jun Liu; Guo Ling Dong; Hong Li Liu

doi:10.4028/www.scientific.net/AMM.701-702.330

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 701-702Research of Image Segmentation Based on Iterative...

Research of Image Segmentation Based on Iterative Threshold

Abstract:

Image segmentation is the key step in image recognition,the result of segmentation affects the one of recognition directly.The article introduces the concept and detailed definition of the image segmentation. The segmentation algorithm of iterative threshold in detail. According to the intrinsic characteristics of weed images, just can use the iteration threshold segmentation method, and implements them by Matlab programme, then processes three weed images, respectively to obtain effective results , and establishes a good base for the pick-up of the target character.

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Periodical:

Applied Mechanics and Materials (Volumes 701-702)

Pages:

330-333

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.701-702.330

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Online since:

December 2014

Authors:

Lei Shao*, Yi Mu, Peng Guo, Jun Liu, Guo Ling Dong, Hong Li Liu

Keywords:

Image Segmentation, Iterative Threshold, MATLAB, Weed Image

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* - Corresponding Author

References

[1] Arman Arefi, Asad Modarres Motlagh, Kaveh Mollazade, Rahman Farrokhi Teimourlou: Recognition and localization of ripen tomato based on machine vision, Australian Journal of Crop Science, vol. 5(10), (2011).

Google Scholar

[2] Wei Ji, Dean Zhao, Fengyi Cheng, Bo Xu, Ying Zhang, Jinjing Wang: Automatic recognition vision system guided for apple harvesting robot, Computers and Electrical Engineering, vol. 38(5), (2012).

DOI: 10.1016/j.compeleceng.2011.11.005

Google Scholar

[3] C.W. Bac, J. Hemming, E.J. van Henten: Robust pixel-based classiﬁcation of obstacles for robotic harvesting of sweet-pepper, Computers and Electronics in Agriculture, vol. 96, (2013).

DOI: 10.1016/j.compag.2013.05.004

Google Scholar

[4] Yoshihiko Mochizuki, Akihiko Torii, Atsushi Imiya: N-Point Hough transform for line detection, Journal of Visual Communication and Image Representation, vol. 20(4), (2009).

DOI: 10.1016/j.jvcir.2009.01.004

Google Scholar

[5] O. Safren, V. Alchanatis, V. Ostrovsky, O. Levi: Detection of Green Apples in Hyperspectral Images of Apple-Tree Foliage Using Machine Vision, Transactions of the ASABE, vol. 50(6), (2007).

DOI: 10.13031/2013.24083

Google Scholar

[6] D. M. Bulanon, T. F. Burks, V. Alchanatis: FRUIT VISIBILITY ANALYSIS FOR ROBOTIC CITRUS HARVESTING, Transactions of the ASABE, vol. 52(1), (2009).

DOI: 10.13031/2013.25933

Google Scholar