Color image segmentation algorithm of corn based on MMC and CV model

被引：0

作者：

Cheng, Yuzhu ^{[1
]}

Chen, Yong ^{[1
]}

Zhang, Hao ^{[1
]}

机构：

[1] College of Mechanical and Electronic Engineering, Nanjing Forestry University

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2013年 / 44卷 / 11期

关键词：

Chan-Vese model; Corn seedling; Image segmentation; Maximum margin criterion;

D O I：

10.6041/j.issn.1000-1298.2013.11.045

中图分类号：

学科分类号：

摘要：

Aiming at removing complex soil background noise in the corn seedling filed, a color image segmentation algorithm based on MMC (Maximum margin criterion) and CV (Chan-Vese) was proposed. The corn color image was transformed into gray image by using MMC, and the grayscale image was denoised by TV (Total variation) filter. Then filtered image was segmented by the CV model. The results of the experiment by Matlab showed that the algorithm could effectively get the extraction of the objection of corn and noise reduction of weed and moss simultaneously in the image. The misclassification rate and the leakage rate were 4.32% and 9.69% respectively, and the similarity was 86.57%.

引用

页码：266 / 270

页数：4

共 16 条

[1]

Karan S., Agrawal K.N., Bora G.C., Advanced techniques for weed and crop identification for site specific weed management, Biosystems Engineering, 109, 1, pp. 52-64, (2011)

[2]

Zhao B., Song Z., Mao W., Et al., Agriculture extra-green image segmentation based on particle swarm optimization and K-means clustering, Transactions of the Chinese Society for Agricultural Machinery, 40, 8, pp. 166-169, (2009)

[3]

Fan D., Yao Q., Yang B., Et al., Progress in research on intelligentization of field weed recognition and weed control technology, Scientia Agricultura Sinica, 43, 9, pp. 1823-1833, (2010)

[4]

Guijarro M., Pajares G., Et al., Automatic segmentation of relevant textures in agricultural images, Computers and Electronics in Agriculture, 75, 1, pp. 75-83, (2011)

[5]

Zhao D., Mao H., Chen S., Et al., Research of different background segmentation algorithms for weed identification, Journal of Agricultural Mechanization Research, 11, pp. 76-79, (2009)

[6]

Mao H., Hu B., Zhang Y., Et al., Optimization of color index and threshold segmentation in weed re-cognition, Transactions of the CSAE, 23, 9, pp. 154-158, (2007)

[7]

Meyer G.E., Neto J.C., Jones D.D., Et al., Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images, Computers and Electronics in Agriculture, 42, 3, pp. 161-180, (2004)

[8]

Ruiz-Ruiz G., Gomez-Gil J., Navas-Gracia L.M., Testing different color spaces based on hue for the environment-ally adaptive segmentation algorithm (EASA), Computers and Electronics in Agriculture, 68, 1, pp. 88-96, (2009)

[9]

Zheng L., Zhang J., Wang Q., Mean-shift-based color segmentation of images containing green vegetation, Computers and Electronics in Agriculture, 65, 1, pp. 93-98, (2009)

[10]

Li H.F., Jiang T., Zhang K.S., Efficient and robust feature extraction by maximum margin criterion, IEEE Transactions on Neural Networks, 17, 1, pp. 157-165, (2006)

← 1 2 →