Abstract
As leaf images can be captured more and more conveniently, automatic leaf recognition has been the key to help us identify different kinds of plant. However, fast and robust leaf recognition is still an unsolved problem, because the leaf images can be collected among different growing stages and with different shapes and colors. In this paper, we present a fast and robust method for leaf recognition by identifying leaves based on rotation invariant shape context (RISC) and summed squared differences (SSD) color matching. Unlike the existing shape context, which is only scale and translational invariant, our proposed method can recognize the leaves with different rotational angles, namely rotation invariant. To distinguish plants having the same shape context but with different colors, we use SSD color matching to measure the similarity of different leaves. The combination of RISC and SSD makes our leaf recognition method faster and much more robust than conventional shape context method. In our experiment, we obtained convincing results to demonstrate its effectiveness.
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Acknowledgments
The authors would like to thank our anonymous reviewers for their valuable comments. This work was supported in part by grants from National Natural Science Foundation of China (No. 61303101, 61170326, 61170077), the Natural Science Foundation of Guangdong Province, China (No. S2012040008028, S2013010012555), the Shenzhen Research Foundation for Basic Research, China (No. JCYJ20120613170718514, JCYJ20130326112201234), the Shenzhen Peacock Plan (No. KQCX20130621101205783), and the Start-up Research Foundation of Shenzhen University (No. 2012-801, 2013-000009).
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Wu, H., Pu, P., He, G., Zhang, B., Yuan, L. (2014). Fast and Robust Leaf Recognition Based on Rotation Invariant Shape Context. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_14
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DOI: https://doi.org/10.1007/978-3-642-54924-3_14
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