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...
Saved in:
| Published in | 农业工程学报 Vol. 33; no. z1; pp. 322 - 327 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| 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 | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2017.z1.048 |
Cover
| Abstract | 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. |
|---|---|
| AbstractList | 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. TP274.5; 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. |
| Abstract_FL | 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 greenhouse. This segmentation algorithm was based on traditional Niblack algorithm using the correlation between global and local grayscale change information of tomato image. The original tomato image was firstly transformed to gray space, and the gray-level image was obtained using the normalized color difference method, and segmented into the foreground and the background. The normalized color difference method could eliminate the light intensity information in the red and green components. Then a new Niblack threshold segmentation algorithm was used to segment the gray image. The adjustment parameter was calculated through the expected value of each window and normalized standard deviation. After denoising, the ripe tomato object could be easily extracted from segmented image by using the minimum critical rectangle method. In order to compare different segmentation algorithms, traditional Niblack algorithm, Otsu algorithm and Niblack self-adaptive adjustment parameter selection algorithm had been selected to perform the comparative analysis. Experiments showed that the Otsu algorithm could extract the target of interest in the image, which contributed significantly to the subsequent target recognition and the reduction in computation time. However, this method may fail to segment overlapping tomatoes into individual ones. For Otsu algorithm, the threshold selection in each region lacked the image characteristics, which caused the binary result to contain a lot of background noise. Traditional Niblack algorithm exaggerated image details and got a lot of unnecessary edge information, which made it difficult to separate the target from background. Niblack self-adaptive adjustment parameter selection algorithm could effectively overcome the problem of pseudo noise. This approach has gotten a good applying result in the extraction of ripe tomato object from original images in greenhouse environment. The accuracy rate of ripe tomato recognition could reach 98.3%. Compared with Otsu algorithm based on normalized difference of red and green, and traditional Niblack segmentation algorithm, segmentation algorithm based on Niblack self-adaptive adjustment parameter selection is more efficient, and its noise is smaller and the process is faster. It can meet the need of the subsequent identification of tomato image and solve the problems of low adaptation and pseudo noise block with traditional methods. But because of the complexity of the object-picking environment, the new algorithm remains to be further improved in the practical application. |
| Author | 王丽丽 魏舒 赵博 毛文华 胡小安 范晋伟 |
| AuthorAffiliation | [1]北京工业大学机械工程及应用电子技术学院,北京100124,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;[2]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;[3]北京工业大学机械工程及应用电子技术学院,北京,100124 |
| AuthorAffiliation_xml | – name: 北京工业大学机械工程及应用电子技术学院,北京100124;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 |
| Author_xml | – sequence: 1 fullname: 王丽丽 魏舒 赵博 毛文华 胡小安 范晋伟 |
| BookMark | eNo9j09LAkEAxedgkJUfIzrtNrO78-8Y0h9B6iJ0lJnZXVuzsVyi9BQh2SHSQ3qog0FdEpLAS9mhL6O79i3aMDq9x-PHe7wlkNJV7QGwiqCJEKd4vWwGYahNBKFlEIa4aUFEzQYyocNSIP2fL4JMGAYSYmRTCB2UBrlpfzwZ3-4GsiLU4aw1-L64nI7vJl_D6PUpHn1G3bf4vhm9v0yHz9F1J77qx93B7KYZPwyjx1bU7kzbvaj3EY26K2DBF5XQy_zpMihsbRayO0Z-bzuX3cgbilnMcIjylKRE2VTYtsAeIoK5kCgiqVTEVQJKC1rCQYpSK_FKMI9D6nMuPeS79jJYm9eeCe0LXSqWq6c1nQwWdb2kzuXv8wZKfickn5PqoKpLJ0HCHteCI1GrF_ddyhiniCRCCE5oh2PMocMdhi1M7B9HEnxP |
| ClassificationCodes | TP274.5 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP ~WA 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.11975/j.issn.1002-6819.2017.z1.048 |
| DatabaseName | 中文期刊服务平台 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库- 镜像站点 Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture |
| DocumentTitleAlternate | Target extraction method of ripe tomato in greenhouse based on Niblack self-adaptive adjustment parameter |
| DocumentTitle_FL | Target extraction method of ripe tomato in greenhouse based on Niblack self-adaptive adjustment parameter |
| EndPage | 327 |
| ExternalDocumentID | nygcxb2017z1048 Wd788971678866504849559049485256 |
| GrantInformation_xml | – fundername: 国家 863 计划项目-设施农业装备的数字化设计与智能控制技术 funderid: (2013AA102406) |
| GroupedDBID | -04 2B. 2B~ 2RA 5XA 5XE 92G 92I 92L ABDBF ABJNI ACGFO ACGFS AEGXH AIAGR ALMA_UNASSIGNED_HOLDINGS CCEZO CHDYS CQIGP CW9 EOJEC FIJ IPNFZ OBODZ RIG TCJ TGD TUS U1G U5N ~WA 4A8 93N ACUHS PSX |
| ID | FETCH-LOGICAL-c828-46cecb76c37a33a5e16a8d06c6b7bc6dca0b202a41c7720b2ca8e907f99be1fd3 |
| ISSN | 1002-6819 |
| IngestDate | Thu May 29 04:08:34 EDT 2025 Wed Feb 14 10:02:35 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | z1 |
| Keywords | 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 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c828-46cecb76c37a33a5e16a8d06c6b7bc6dca0b202a41c7720b2ca8e907f99be1fd3 |
| Notes | 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 |
| PageCount | 6 |
| ParticipantIDs | wanfang_journals_nygcxb2017z1048 chongqing_primary_Wd788971678866504849559049485256 |
| PublicationCentury | 2000 |
| PublicationDate | 2017 |
| PublicationDateYYYYMMDD | 2017-01-01 |
| PublicationDate_xml | – year: 2017 text: 2017 |
| PublicationDecade | 2010 |
| PublicationTitle | 农业工程学报 |
| PublicationTitleAlternate | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationTitle_FL | Transactions of the Chinese Society of Agricultural Engineering |
| PublicationYear | 2017 |
| Publisher | 北京工业大学机械工程及应用电子技术学院,北京100124 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 |
| Publisher_xml | – name: 北京工业大学机械工程及应用电子技术学院,北京100124 – name: 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 |
| SSID | ssib051370041 ssib017478172 ssj0041925 ssib001101065 ssib023167668 |
| Score | 2.1773198 |
| Snippet | 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... TP274.5; 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... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 322 |
| SubjectTerms | 图像处理;提取;算法;番茄;Niblack;自适应修正系数 |
| Title | 基于Niblack自适应修正系数的温室成熟番茄目标提取方法 |
| URI | http://lib.cqvip.com/qk/90712X/2017z1/Wd788971678866504849559049485256.html https://d.wanfangdata.com.cn/periodical/nygcxb2017z1048 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Academic Search Ultimate issn: 1002-6819 databaseCode: ABDBF dateStart: 20140101 customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn isFulltext: true dateEnd: 99991231 titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn omitProxy: true ssIdentifier: ssj0041925 providerName: EBSCOhost |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1NaxNBdGhTED2In1i_KOKcQuLOfszOHHeTDVWwp4q9hdnJbuol1ZqC5iRSrAexPdge9FBBLxYsQi9aD_6ZZlP_he_NbtIixY9C2Axv39e8t7vvzTDzhpCbLSeJXcjjKwyCU8VViagolVoV6dsxkz6z0wQn9O_O8Ol77p05b25s_MahVUtL3biqe0fuKzmOVwEGfsVdsv_h2RFTAEAb_AtX8DBc_8nHNPKobNAwoJGLVxHNPIhxRo5GgsIoP4AbEtcyCBtRAUO6BrVBg4hGnAZ1Gjg08mno0DBEiAQ0CyES2LkICQUNJJIDSWAgQlBpcARH8YjsGVkgtGaoABIORVioCVI1DJVnGtzIAubSiHBofgbmME02aIBQM9oKVAb1hx55yDwAQaFRCfQ3rEQAt4YPj1EtMiiGOqyPGmU0CFCBDqgtcLbL2Ao9_KHUOggrG8Ub2IlcTeyBuScigy4cGjCjkmVYGeMIWR6awDF0cqhCDcx0eHol30daxAIMFlwUX_QiWORVO4qXoseODkLS90wUQjbVERtcR-hXe6xq5bVFf6vzfb_lC4EFveCPQ97sCherA-b1eyAxHScTNs45lchEENbDxkHSy3BcP_oqMzwRgR3shrax1gE_GFR6zMEjDUYLoXAZgGfWBBR6niB02Itbf-oDViOZX-i0H0GOZba8dVLVaR_KzmbPkNPFsGoqyN-Rs2SsN3-OnArai0VpmeQ8ud3f3N3bfV28IfsrWz-fPe_vvtn7sZ19_jDY-Z6tfxm8Xc6-fupvf8xerg1ebA7Wt_ZfLQ_ebWfvV7LVtf7qRrbxLdtZv0BmG9FsbbpSHCRS0QInj7lOdOxz7fjKcZSXMK5Ey-Kax36seUsrK7YtW7lMw1gT2lqJRFp-KmWcsLTlXCSlzkInuUSmfJ60Ui24YkK6kPzGqZ1CUNQJVzjLpCaJPbJJ82FeL6b5N89OkqnCeM3i4_K42Xna1k9itHaPAcHl4_C9Qk4ig3zC8CopdReXkmuQQnfj68Uz9At2OJf9 |
| linkProvider | EBSCOhost |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8ENiblack%E8%87%AA%E9%80%82%E5%BA%94%E4%BF%AE%E6%AD%A3%E7%B3%BB%E6%95%B0%E7%9A%84%E6%B8%A9%E5%AE%A4%E6%88%90%E7%86%9F%E7%95%AA%E8%8C%84%E7%9B%AE%E6%A0%87%E6%8F%90%E5%8F%96%E6%96%B9%E6%B3%95&rft.jtitle=%E5%86%9C%E4%B8%9A%E5%B7%A5%E7%A8%8B%E5%AD%A6%E6%8A%A5&rft.au=%E7%8E%8B%E4%B8%BD%E4%B8%BD+%E9%AD%8F%E8%88%92+%E8%B5%B5%E5%8D%9A+%E6%AF%9B%E6%96%87%E5%8D%8E+%E8%83%A1%E5%B0%8F%E5%AE%89+%E8%8C%83%E6%99%8B%E4%BC%9F&rft.date=2017&rft.issn=1002-6819&rft.volume=33&rft.issue=z1&rft_id=info:doi/10.11975%2Fj.issn.1002-6819.2017.z1.048&rft.externalDocID=Wd788971678866504849559049485256 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F90712X%2F90712X.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fnygcxb%2Fnygcxb.jpg |