Flower Bud Detection Based on Saliency Map and SURF Feature-Points

You Jun Yue; Xiang Li; Hui Zhao

doi:10.4028/www.scientific.net/AMM.740.656

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 740Flower Bud Detection Based on Saliency Map and...

Flower Bud Detection Based on Saliency Map and SURF Feature-Points

Abstract:

The flower bud ratio is an important criteria and nodus in flower automatic grading realization, however using computer vision to detect flower bud still remains a challenge. This paper presents a flower bud detection model as a resolution: By using spectral residual method to calculate saliency map and using SURF feature-points, we can quickly and unambiguously obtain the SURF histogram, then construct the flower bud detection model with SVM. The experiments indicates that the flower bud detection model which provides an effective way for measuring bud ratio in flower automatic grading system can distinguish between bud and bloom with good results.

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Periodical:

Applied Mechanics and Materials (Volume 740)

Pages:

656-659

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.740.656

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Online since:

March 2015

Authors:

You Jun Yue*, Xiang Li, Hui Zhao

Keywords:

Flower Bud Detection, Image Recognition, Saliency Map, SURF Feature-Point

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* - Corresponding Author

References

[1] Meiling Shi, The study on export situation of China flower products, World Agriculture, 2012, pp.134-137. In Chinese.

Google Scholar

[2] Wei, Jyun-han, Wu, Shih-Hung, Liang-Pu Chen, Wen-Tai Hsieh, Seng-cho T. Chou, Robust object finding vision system based on saliency map analysis, Awareness Science and Technology, International Conference on , 2012, pp.67-72.

DOI: 10.1109/icawst.2012.6469591

Google Scholar

[3] Yi Wang, Xin Fan, Haojie Li, Qi Jia, A Hybrid Visual Attention Method Based on Itti Model, Workshop on Digital Media and Digital Content Management, 2011, pp.261-265.

DOI: 10.1109/dmdcm.2011.41

Google Scholar

[4] Itti, L., Koch, C., Niebur, E., A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, pp.1254-1259.

DOI: 10.1109/34.730558

Google Scholar

[5] Xiaodi Hou, Liqing Zhang, Saliency Detection: A Spectral Residual Approach, IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp.1-8.

DOI: 10.1109/cvpr.2007.383267

Google Scholar

[6] Willy, D., Noviyanto, A., Arymurthy, A.M., Evaluation of SIFT and SURF features in the songket recognition, International Conference on Advanced Computer Science and Information Systems, 2013, pp.393-396.

DOI: 10.1109/icacsis.2013.6761607

Google Scholar

[7] Shiqiang Li, Tianding Chen, Changhong Yu, SURF-based video mosaic and its key technologies, Journal of Computational Information Systems, 2010, pp.3267-3276.

Google Scholar

[8] Swin Michael. J., Ballard Dana H., Color Idexing, International Journal of Computer Vision, 1991, pp.11-32.

Google Scholar