融合马尔可夫随机场与量子粒子群聚类的棉花图像分割算法

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

【目的】提高采摘机器人棉花图像处理系统中的图像分割准确率。【方法】提出融合马尔可夫随机场与量子粒子群聚类的图像分割算法。步骤:将读入的RGB模式彩色图像转换成灰度图像;利用本算法分割图像;通过对连通区域面积阈值的设定获取目标区域。使用本算法分割不同角度采集的棉花正面图像与棉花侧面图像,并采用分割精度、峰值信噪比,分别与Otsu算法、模糊聚类图像分割算法、量子粒子群图像分割算法和马尔可夫随机场图像分割算法进行效果比较。【结果】本算法分割精度、峰值信噪比平均值分别为98.94%、77.48dB,与Otsu算法、模糊聚类图像分割算法、量子粒子群图像分割算法、马尔科夫随机场图像分割算法相比,分割精度、峰值信噪比分别提高2.47~4.56百分点、9.81~13.11 dB。【结论】本算法处理棉花图像具有更高的分割精度以及峰值信噪比。

[Objective]The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system.[Method]An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed.The process of the proposed algorithm is as follows:first,transform the RGB(red,green,blue)images into grayscale;second,use it to segment these images;finally,the threshold of the connected area is set on the basis of the segmented image to obtain the target area.Then,the cotton front image and the cotton side image are selected from the images collected from different angles.The segmentation experiment was carried out by using this algorithm,and compared with the Otsu algorithm,the fuzzy C-means algorithm,the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm.[Result]The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94%and 77.48 dB.When compared with the Otsu algorithm,fuzzy C-means algorithm,quantum particle swarm optimization algorithm and Markov random field algorithm,the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%,and 9.81–13.11 dB,respectively.[Conclusion]The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.