• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.780-791
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• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.715-721
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• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.

• Journal of Biosystems Engineering
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• 제30권6호통권113호
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• pp.340-346
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• 2005

Major quality features of the beef carcass in most countries including Korea are size, marbling state of the lean tissue, color of the fat and lean tissue, and thickness of back fat of the 13th rib. To evaluate the beef quality, extracting loin parts from the sectional image of the 13th beef rib is crucial and is the first step. However, because of the inhomogeneous distribution and fuzzy pattern of the fat and lean tissues on the beef cut, it is difficult to extract automatically the proper contour of the lean tissue. In this paper, a prototype mobile beef quality measurement system, which can be implemented practically at the beef processing site was developed. The developed system was composed of the hand held image acquisition unit and mobile processing unit mounted with touch-pad screen. Algorithms to extract the boundary of the lean tissue and a proper tool to evaluate the marbling status have been developed using color image processing. The boundary extraction algorithm showed successful results for the beef cuts with simple and moderate patterns of the lean tissue and fat. However, it had some difficulty in eliminating complex pattern of the extraneous tissues adhered to the lean tissue in the boundary extraction. The developed algorithms were implemented to the prototype mobile processing unit.

• 한국식품과학회지
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• 제28권4호
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• pp.786-789
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• 1996

식품 표면의 색변화를 화상화하고 3원색으로 분리하여 가공, 처리할 수 있는 영상 처리 시스템을 video camera와 영상 카드, 조명 장치, PC로 구성하였다. Video camera로부터 출력되는 analog 화상 신호를 영상카드에서 digital신호로 변환하고 이를 PC 모니터 상에서 $640{\times}480$ 해상도의 자연색으로 출력할 수 있도록 하였다. C 언어로 작성한 프로그램에 의하여 일정한 시간 간격으로 출력 화면을 파일로 저장하고 여러 가지 화상 분석을 수행할 수 있도록 하였다. 이 영상 처리 시스템을 사용하여 사과의 숙도 차이를 색차이로 분석한 결과 미숙 완숙의 정도를 green과 red 성분의 색값 차이로 나타낼 수 있었으며, blue 성분의 차이는 미미하였다. green 성분의 차이는 35.01이고 red 성분의 차이는 6.16으로 나타나, 사과의 분급에는 green색을 이용하는 것이 적합하였다. 고기의 육질과 지방의 색차를 이용한 화상 분리에서는 육질 부분에서의 red성분이 $180{\sim}230$인 반면에 지방은 240 이상으로 나타나, red 성분을 기준으로 한 경계값을 사용하여 육질과 지방의 화상을 분리하여 육질의 색 성분을 정확히 측정할 수 있었다. 이와 같이 육질의 조직별로 분리한 후의 Hunter값은 전체 고기를 대상으로 할 때는 L, a, b값이 70.6, 38.4, 22.8이지만 육질 부분만으로 했을 때 L, a, b값은 65.6, 44.4, 21.3이었고, 색차값 ${\Delta}E$가 2%감소하였다.