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This paper presents a novel hand segmentation method in complex environment based on color model and boundary cutting. Probability map is generated through skin color model to get a rough segmentation. For boundary cutting, a vote map based method is proposed to get precise hand segmentation. Experimental result is appraised by sensitivity and specificity which are 96.54% and 96.92% respectively.
We search for an improved gray GM (1,1) model by data processing. First, a number of properties of generation by weighting are proved, and then weighting generating is applied to GM(1,1). Calculation result indicates that weighting GM(1,1) model is improved and precision is increased. This indicated that weighting GM(1,1) model has better applicability.
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