Grain bags detection based on improved maximum between-cluster variance algorithm
The key to extracting the edge characteristics of grain bags in the grain reserve warehouse was image segmentation. In practice, the quality of the selected segmentation algorithm directly determined the effect of the bags image segmentation. According to the characteristic of the grain warehouse scene an improved segmentation algorithm based on combining maximum between-cluster variance with edge detection method was proposed to achieve bags edge accurately detecting results as far as possible. First of all, the improved Otsu algorithm which could effectively confirm the bags objective was used to segment the actual scene image initially. And then, comparing with the results of the classical edge detection operator, the bags outline could be efficiency extracted by Canny operator. Experimental results showed that the proposed algorithm could effectively extract the bags outline, and had the merits of high precision and strong robustness. This work provided a grain bags detection method that laid a good foundation for the further work of bags intelligent identification reckoning.
grain bags image segmentation edge detection maximum between-cluster variance algorithm
Yong Liu Sha Chen Ying Lin
School of management, Chongqing Jiaotong University Chongqing, 400074, China School of management, Chongqing Jiaotong University, Chongqing, 400074, China
国际会议
太原
英文
274-277
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）