An edge detection for sunflower seeds based on improved Canny algorithm
Vol: 31, Issue: 5, Page: 36-38
2015
- 43Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage43
- Downloads27
- Abstract Views16
Artifact Description
Aiming at the edge-detection for a class of objects of irregular shape, such as sunflower seeds, but the traditional Canny algorithm has many shortcomings. This paper proposed three improvements: ① The adaptive high threshold method based on the gradient statistic difference was to overcome the problem that the traditional Canny algorithm might lose part valid edge information when the gray of goals and background in the image had a relatively wide change, which had higher signal-to-noise ratio; ② It proposed an algorithm of extending at the endpoints based on the gradient direction was made up for the defect of getting many interrupted edges after the edge link analysis of traditional Canny algorithm; ③ It proposed regional bounding algorithm based on the longest edge to remove the short edge of background for achieving the precise positioning of goals edges. The results demonstrated that improved Canny algorithm achieved good results in edge-detection of sunflower seeds in high complex background.
Bibliographic Details
https://www.ifoodmm.cn/journal/vol31/iss5/8; https://www.ifoodmm.cn/cgi/viewcontent.cgi?article=4756&context=journal; http://dx.doi.org/10.13652/j.issn.1003-5788.2015.05.008; https://dx.doi.org/10.13652/j.issn.1003-5788.2015.05.008; https://www.chndoi.org/Resolution/Handler?doi=10.13652/j.issn.1003-5788.2015.05.008
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