Shucked corn detection based on GMM and LBP features

文献类型： 外文期刊

第一作者： Liu, Zechuan

作者： Liu, Zechuan;Wang, Song;Zhang, Dayong;Ling, Qiang;Han, Keli;Han, Zengde

作者机构：

关键词： Corn detection; GMM; LBP; SVM

期刊名称：PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019)

ISSN： 1948-9439

年卷期： 2019 年

页码：

收录情况： SCI

摘要： Real-time detection of the degree of corn peeling is important to determine the operational status of the corn harvester. This paper presents a method to detect shucked corn. First, moving objects are detected from the background by using Gaussian Mixture Model (GMM) and morphological operations. Then, texture features, called Local Binary Pattern(LBP) features, are computed from multi-scale foreground images. Finally these texture features are sent to a trained support vector machine, which makes the decision whether a corn is shucked or not. Owing to the post-processing on the foreground image segmented after background modeling, our method can filter out redundant noise points. Due to the prominent difference of the LBP features of different objects, our method can make classification more robustly. Therefore, our method is accurate and efficient in the task of shucked corn detection, which is confirmed by experimental results.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序