• 研究简报 •

### 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

1. 1.郑州大学信息工程学院，郑州 450052； 2.河南机电职业学院信息系，郑州451191
• 收稿日期:2017-05-10 出版日期:2018-03-15 发布日期:2018-03-15
• 作者简介: 龙金辉（1964―），男，硕士，副教授，llongjh@sina.com
• 基金资助:
国家自然科学基金(61170223)；国家自然科学基金联合基金(U1204610)

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

Long Jinhui1,2, Zhu Zhenfeng1

1. 1. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China; 2. Department of Information Engineering, Henan Machinery and Electronics Vocational College, Zhengzhou 451191, China
• Received:2017-05-10 Online:2018-03-15 Published:2018-03-15

Abstract: [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.

• S562：TP391.4