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Automatic Classification Algorithm for Raw Materials using Mean Shift Clustering and Stepwise Region Merging in Color
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  • Journal title : Journal of Broadcast Engineering
  • Volume 21, Issue 3,  2016, pp.425-435
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2016.21.3.425
 Title & Authors
Automatic Classification Algorithm for Raw Materials using Mean Shift Clustering and Stepwise Region Merging in Color
Kim, SangJun; Kwak, JoonYoung; Ko, ByoungChul;
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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.
Raw material classification;mean shift clustering;stepwise region merging;2-D color distribution;
 Cited by
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