基于机器视觉和支持向量机的温室黄瓜识别

In-greenhouse Cucumber Recognition Based on Machine Vision and Least Squares Support Vector Machine

针对复杂温室环境中的成熟黄瓜,采用脉冲耦合神经网络分割黄瓜图像,利用数学形态学方法处理,把黄瓜从图像背景中分离出来;提取各连通区域的4个几何特征值和灰度共生矩阵基础上的3个纹理特征值,作为最小二乘支持向量机(LS-SVM)的输入特征向量;利用训练好的分类器判别图像中的黄瓜。试验结果表明:用于试验的70幅黄瓜图像,正确识别率达82.9%,基于脉冲耦合神经网络分割结合LS-SVM的方法,适合复杂背景的温室黄瓜识别。

Pulse coupled neural network （PCNN） and mathematical morphological technologies were employed to separate the mature in-greenhouse cucumber from complex background image. Four geometric feature values and three texture feature values based on gray level co-occurrence matrix （GLCM） of every connected regions in image were extracted, which were the input feature vector of least squares support vector machine （ LS - SVM）. The trained classifier was used for identifying the cucumber in image. Experimental results showed that 70 cucumber images were used for testing, the average rate of correct identification reach to 82.9% in different conditions, indicating that the method based on PCNN and LS- SVM could be used for in-greenhouse cucumber recognition.