Study on background segment and edge detection of fruit image using machine vision
In the light of the characteristics of gray level variations in the image, bimodal gray region segment method was adopted to segment the pear from the background in the three images (red, green, blue). The best segment result was appeared in B gray level histogram. Four different edge operator were...
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| Format: | Article |
| Language: | English |
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Zhejiang University Press
2000-01-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/1008-9209.2000.01.0035 |
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| _version_ | 1849247237047058432 |
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| author | YING Yi-bin |
| author_facet | YING Yi-bin |
| author_sort | YING Yi-bin |
| collection | DOAJ |
| description | In the light of the characteristics of gray level variations in the image, bimodal gray region segment method was adopted to segment the pear from the background in the three images (red, green, blue). The best segment result was appeared in B gray level histogram. Four different edge operator were used to extract the step-like edge of the fruit image, and the Hilditch thinning method was used to fulfil edge thinning. As the results shown, the adopted method of background segment and edge detection could satisfy the requirement of detecting size, shape, stem, and surface defect of fruit. |
| format | Article |
| id | doaj-art-3e4f17fa02c8466f8a74b9675108aafd |
| institution | Kabale University |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2000-01-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-3e4f17fa02c8466f8a74b9675108aafd2025-08-20T03:58:17ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552000-01-0126353810.3785/1008-9209.2000.01.003510089209Study on background segment and edge detection of fruit image using machine visionYING Yi-binIn the light of the characteristics of gray level variations in the image, bimodal gray region segment method was adopted to segment the pear from the background in the three images (red, green, blue). The best segment result was appeared in B gray level histogram. Four different edge operator were used to extract the step-like edge of the fruit image, and the Hilditch thinning method was used to fulfil edge thinning. As the results shown, the adopted method of background segment and edge detection could satisfy the requirement of detecting size, shape, stem, and surface defect of fruit.https://www.academax.com/doi/10.3785/1008-9209.2000.01.0035fruitsshapebackground segmentedge extractionthinningimagemachine vision |
| spellingShingle | YING Yi-bin Study on background segment and edge detection of fruit image using machine vision 浙江大学学报. 农业与生命科学版 fruits shape background segment edge extraction thinning image machine vision |
| title | Study on background segment and edge detection of fruit image using machine vision |
| title_full | Study on background segment and edge detection of fruit image using machine vision |
| title_fullStr | Study on background segment and edge detection of fruit image using machine vision |
| title_full_unstemmed | Study on background segment and edge detection of fruit image using machine vision |
| title_short | Study on background segment and edge detection of fruit image using machine vision |
| title_sort | study on background segment and edge detection of fruit image using machine vision |
| topic | fruits shape background segment edge extraction thinning image machine vision |
| url | https://www.academax.com/doi/10.3785/1008-9209.2000.01.0035 |
| work_keys_str_mv | AT yingyibin studyonbackgroundsegmentandedgedetectionoffruitimageusingmachinevision |