Modified Lawn Weed Detection: Utilization of Edge-Color Based SVM and Grass-Model Based Blob Inspection Filterbank

نویسندگان

  • Ukrit Watchareeruetai
  • Yoshinori Takeuchi
  • Tetsuya Matsumoto
  • Hiroaki Kudo
  • Noboru Ohnishi
چکیده

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., supportvector 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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تاریخ انتشار 2007