An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Digital Image Processing
Computer vision based methods for detecting weeds in lawns
Machine Vision and Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.