杂草识别中颜色特征和阈值分割算法的优化

在机器视觉识别杂草中，分割误差对识别精度的影响日益突出。提出将分割中使用的颜色特征和阈值转换为RGB颜色空间中的一个分割面，引入Bayes理论建立了分割误差的评价方法，采用遗传算法优化选择分割面，由此优化得到的分割面为-149R+218G-73B=127。试验结果表明：与超绿特征相比，该方法分割后的噪声小，平均分割误差概率从3.90%降低到2.33%，更利于提取用于识别的形态特征。

Optimization of color index and threshold segmentation in weed recognition

The impact of potential classification error on machine-vision weed recognition has stimulated research into new methods of optimizing segmentation. The color index and threshold for weed image segmentation are transformed into the segmentation surface in RGB color space. The evaluating method of segmentation error was established with Bayes formula, and color indexes were optimized and threshold parameter was processed via genetic algorithm. Optimal segmentation surface is -149R+218G-73B=127. With a comparison of the experimental results between Excess-Green method and new segmentation surface method, the segmentation noise of the new method is lower than the former, the average probability of segmentation error decreases from 3.90% to 2.33%. It is more propitious to the extraction of shape feature in the next classification operation.