Abstract
We propose a lawn weed detection method modified from our previous work, i.e., Bayesian classifier based method. The proposed method employs features calculated from not only the edge-strength of weed/lawn textures but also color information of RGB. Instead of using Bayesian classifier, we exploit more sophisticated classifier, i.e., support-vector machine, for detecting weeds. After weed detection, the proposed method uses noise blob inspection for removing misclassified weed areas. The inspection process is based on a bank of directional filters modeled from characteristics of the edge of grass blade. Experimental results show that the performance of the proposed method outperforms the compared methods.
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Watchareeruetai, U., Takeuchi, Y., Matsumoto, T., Kudo, H., Ohnishi, N. (2008). Modified Lawn Weed Detection: Utilization of Edge-Color Based SVM and Grass-Model Based Blob Inspection Filterbank. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_4
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DOI: https://doi.org/10.1007/978-3-540-69162-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
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