基于Niblack自适应修正系数的温室成熟番茄目标提取方法

番茄目标的准确提取是番茄采摘的基础,目前番茄目标提取方法都有一定的局限性,难以满足采摘需求.该研究在传统Niblack算法的基础上,结合图像全局灰度变化的估计信息与局部区域信息之间的关联性,提出了一种基于Niblack自适应修正系数的温室成熟番茄目标提取新方法.首先对R-G番茄灰度图像,采用基于自适应修正系数选取的Niblack算法进行阈值分割,从理论意义上确定修正系数的选取原则,归一化局部标准差,实现修正值的计算及二值化过程,然后对分割后的图像去噪,最后采用最小临界矩形法提取成熟番茄果实.试验结果表明,该方法对温室成熟番茄图像有较好的提取效果,识别正确率达到98.3%,与基于归一化红绿色差灰...

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Bibliographic Details
Published in农业工程学报 Vol. 33; no. z1; pp. 322 - 327
Main Author 王丽丽 魏舒 赵博 毛文华 胡小安 范晋伟
Format Journal Article
LanguageChinese
Published 北京工业大学机械工程及应用电子技术学院,北京100124 2017
中国农业机械化科学研究院土壤植物机器系统技术国家重点实验室,北京100083%中国农业机械化科学研究院土壤植物机器系统技术国家重点实验室,北京,100083%北京工业大学机械工程及应用电子技术学院,北京,100124
Subjects
图像处理;提取;算法;番茄;Niblack;自适应修正系数
算法
algorithms
番茄
Image processing
图像处理
自适应修正系数
提取
tomato
extraction
adaptive adjustment parameter
Niblack
Online AccessGet full text
ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2017.z1.048

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Summary:番茄目标的准确提取是番茄采摘的基础,目前番茄目标提取方法都有一定的局限性,难以满足采摘需求.该研究在传统Niblack算法的基础上,结合图像全局灰度变化的估计信息与局部区域信息之间的关联性,提出了一种基于Niblack自适应修正系数的温室成熟番茄目标提取新方法.首先对R-G番茄灰度图像,采用基于自适应修正系数选取的Niblack算法进行阈值分割,从理论意义上确定修正系数的选取原则,归一化局部标准差,实现修正值的计算及二值化过程,然后对分割后的图像去噪,最后采用最小临界矩形法提取成熟番茄果实.试验结果表明,该方法对温室成熟番茄图像有较好的提取效果,识别正确率达到98.3%,与基于归一化红绿色差灰度化的Otsu算法和传统的Niblack算法相比有更高的识别率和更快的处理速度,噪声率也明显减少,能够满足后续成熟番茄定位的需要,有效地解决传统方法适应性低,易产生伪噪声块等问题.
Bibliography:Image processing;extraction;algorithms;tomato;Niblack;adaptive adjustment parameter
11-2047/S
Tomato is one of the most popular and widely grown vegetables in the world. Manual harvesting of tomatoes is laborious, time-consuming and inefficient, thus making it somewhat impractical for large-scale plantations. Intelligent robots have been developed for harvesting tomato. However, as the tomato is very soft and thus especially prone to bruising, many significant technical challenges remain to be solved. In China, the research on the harvesting robot is still in its infancy, but considerable progress has been made in many aspects, such as the manipulator, image recognition, and motion control. Tomato targets extraction is the basis for location and picking of tomato. Early extraction methods have certain limitations, which are difficult to meet the demand of harvest. In this study, Niblack self-adaptive adjustment parameter selection method was put forward and successfully applied in extracting ripe tomato in gree
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.z1.048