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Skin-color detection based on adaptive thresholds

机译:基于自适应阈值的肤色检测

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In this paper a new skin detection method based on adaptive thresholds is proposed. As compared with the fixed threshold histogram widely method used, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A novel image relation operation is also proposed to eliminate the confusing backgrounds. The method is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.
机译:提出了一种新的基于自适应阈值的皮肤检测方法。与广泛使用的固定阈值直方图方法相比,我们可以找到针对不同复杂背景的最佳阈值。从皮肤概率分布直方图(SPDH)中总结了四个线索,以帮助搜索最佳阈值的候选者,并且训练了ANN分类器以选择最终的最佳阈值。还提出了一种新颖的图像关联操作来消除令人困惑的背景。由于不涉及迭代操作,因此该方法快速,因此适用于实时应用。实验结果表明,与固定阈值直方图方法相比,该方法具有更好的性能。

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