Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disea...Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper.The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure.Firstly,a coarse decision tree was built by the CART(Classification and Regression Tree)algorithm with a feature subset.The feature subset consisted of color features that was selected by Pearson’s Rank correlations.Then,the coarse decision tree was optimized by pruning.Using the optimized decision tree,segmentation of disease symptom images was achieved by conducting pixel-wise classification.In order to evaluate the robustness and accuracy of the proposed method,an experiment was performed using greenhouse cucumber downy mildew images.Results showed that the proposed method achieved an overall accuracy of 90.67%,indicating that the method was able to obtain robust segmentation of disease symptom images.展开更多
A one-dimensional （1D） top-hat, diode-end-pumped, electro-optically Q-switched Nd：YVO4 slab laser is demonstrated. Under the pump power of 175.5 W, 26.3-W output power is obtained with the repetition rate of 5 kHz ...A one-dimensional （1D） top-hat, diode-end-pumped, electro-optically Q-switched Nd：YVO4 slab laser is demonstrated. Under the pump power of 175.5 W, 26.3-W output power is obtained with the repetition rate of 5 kHz and the pulse width of 4.3 ns. The output beam has a good top-hat beam profile in both the near field and the far field.展开更多
基金The authors would like to thank the financial support provided by The National Key Research and Development Program of China(2016YFD0300606,2017YFD0300402 and 2017YFD0300401).
文摘Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper.The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure.Firstly,a coarse decision tree was built by the CART(Classification and Regression Tree)algorithm with a feature subset.The feature subset consisted of color features that was selected by Pearson’s Rank correlations.Then,the coarse decision tree was optimized by pruning.Using the optimized decision tree,segmentation of disease symptom images was achieved by conducting pixel-wise classification.In order to evaluate the robustness and accuracy of the proposed method,an experiment was performed using greenhouse cucumber downy mildew images.Results showed that the proposed method achieved an overall accuracy of 90.67%,indicating that the method was able to obtain robust segmentation of disease symptom images.
文摘A one-dimensional （1D） top-hat, diode-end-pumped, electro-optically Q-switched Nd：YVO4 slab laser is demonstrated. Under the pump power of 175.5 W, 26.3-W output power is obtained with the repetition rate of 5 kHz and the pulse width of 4.3 ns. The output beam has a good top-hat beam profile in both the near field and the far field.
