| 저자(한글) |
Cho, Sung-Ho,Huan, Le Ngoc,Choi, Sun,Kim, Tae-Jung,Shin, Wu-Hyun,Hwang, Heon |
| 저자(영문) |
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| 소속기관 |
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| 소속기관(영문) |
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| 출판인 |
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| 간행물 번호 |
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| 발행연도 |
2014-01-01 |
| 초록 |
Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site. |
| 원문URL |
http://click.ndsl.kr/servlet/OpenAPIDetailView?keyValue=03553784&target=NART&cn=JAKO201427542600414 |
| 첨부파일 |
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