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题名: 基于PCNN的面粉麸星检测方法
其他题名: Bran Specks Detection Approach Based on PCNN
作者: 陈天飞; 吴翔; 刘楠嶓; 李秀娟
作者部门: 装备制造技术研究室
关键词: 视觉检测 ; 面粉麸星 ; 灰度熵变换 ; PCNN
刊名: 中国粮油学报
ISSN号: 1003-0174
出版日期: 2015
卷号: 30, 期号:12, 页码:136-139
收录类别: EI ; CSCD
产权排序: 1
项目资助者: 河南省教育厅科学技术研究重点项目(14B413001,14A413004) ; 河南工业大学高层次人才基金(2014BS008)
摘要: 面粉加工过程中麸星数目的多少直接影响着面粉的品质等级,为此,本研究提出了一种基于脉冲耦合神经网络(PCNN)的图像处理方法实现对面粉中微小麸星的视觉检测。首先,该方法对采集的面粉图像进行局部灰度熵变换并通过比例映射生成熵值图像,从而完成了原始面粉图像的图像增强。然后,在图像增强的基础上,利用PCNN对熵值图像进行迭代处理,并通过最小交叉熵确定最优迭代次数,完成最终的麸星目标分割。最后试验验证了该方法的有效性,对比结果表明该方法的检测灵敏度提高近2倍,且算法运行时间为5.189 3s,具有较高的执行效率。
英文摘要: Due to the fact that the number of bran specks has directly influence on the flour quality level,so a novel detection approach based on pulse coupled neural network ( PCNN) was proposed so as to achieve vision detection for tiny bran specks in flour.Firstly,the local grey scale entropy transformation was carried on for the original captured image,and the entropy image was formed through proportional mapping,so the bran specks in flour could be enhanced in the entropy image.Secondly,on the basis of image enhancement,the PCNN was applied on the entropy image,and final segmentation results can be obtained after iterative processing,while the optimal number of iterations was determined by the minimum cross entropy.Finally,the actual experimental results demonstrated that the proposed method was effective,and its detection sensitivity was improved nearly twice.In addition,the method runs for 5. 189 3s,and has better execution efficiency.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17625
Appears in Collections:装备制造技术研究室_期刊论文

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Recommended Citation:
陈天飞,吴翔,刘楠嶓,等. 基于PCNN的面粉麸星检测方法[J]. 中国粮油学报,2015,30(12):136-139.
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