• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.7-14
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• 2008

To nurture skilled IT professionals required in the 21st information society, we have been faced to the need of learner-centered open lifelong education through the e-learning system that crosses the boundaries of time, space and geography. The object of this research is primarily as-is analysis of the IT contents in present cyber institutions, then making out the basic research data by to-be analysis and international standardization trend through benchmarking. Eventually this research intends to produce a prototype of the contents especially for the subjects related to the IT which is based on the modeling of e-Learning standard contents development model. It is possible that the final output of this project is used for the contents producing and operating data in cyber universities and institutions. This output also enhances the international competitiveness by the standardization and presents a standardized guideline to the domestic cyber education institutions.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.