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21 February 1996 Hybrid image processing for robust extraction of lean tissue on beef cut surface
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Proceedings Volume 2665, Machine Vision Applications in Industrial Inspection IV; (1996) https://doi.org/10.1117/12.232244
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
A hybrid image processing system which automatically separates lean tissues from the beef cut surface image and generates the lean tissue contour has been developed. Because of the inhomogeneous distribution and fuzzy pattern of fat and lean tissues on the beef cut, conventional image segmentation and contour generation algorithms suffer from heavy computing, algorithm complexness, and even poor robustness. The proposed system utilizes an artificial neural network to enhance the robustness of processing. The system is composed of three procedures such as pre-network, network based lean tissue segmentation and post- network procedure. At the pre-network stage, gray level images of beef cuts were segmented and resized appropriate to the network inputs. Features such as fat and bone were enhanced and the enhanced input image was converted to the grid pattern image, whose grid was formed as 4 by 4 pixel size. At the network stage, the normalized gray value of each grid image was taken as the network input. Pre-trained network generated the grid image output of the isolated lean tissue. A sequence of post-network processing was followed to obtain the detailed contour of the lean tissue. The training scheme of the network and separating performance were presented and analyzed. The developed hybrid system shows the feasibility of the human like robust object segmentation and contour generation for the complex fuzzy and irregular image.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Heon Hwang, Bosoon Park, Minh Duc Nguyen, and Yud-Ren Chen "Hybrid image processing for robust extraction of lean tissue on beef cut surface", Proc. SPIE 2665, Machine Vision Applications in Industrial Inspection IV, (21 February 1996); https://doi.org/10.1117/12.232244
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