Abstract
We provide an alternative methodology for vegetation segmentation in cornfield images. The process includes two main steps, which makes the main contribution of this approach: (a) a low-level segmentation and (b) a class label assignment using Bag of Words (BoW) representation in conjunction with a supervised learning framework. The experimental results show our proposal is adequate to extract green plants in images of maize fields. The accuracy for classification is 95.3 % which is comparable to values in current literature.
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Acknowledgments
H. Sossa thanks CONACyT under call: Frontiers of Science (grant number 65) for the economic support. We would like to express our sincere gratitude to Jena University research team for their fruitful comments and suggestions for significant improvement of this work, especially to Sven Sickert who help providing results with ICF algorithm.
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Campos, Y., Rodner, E., Denzler, J., Sossa, H., Pajares, G. (2016). Vegetation Segmentation in Cornfield Images Using Bag of Words. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_18
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