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Skin segmentation is widely used in many computer vision tasks to improve automated visualization. This paper presents a graph cuts algorithm to segment arbitrary skin regions from images. The detected face is used to determine the foreground skin seeds and the background non-skin seeds with the color probability distributions for the foreground represented by a single Gaussian model and for the background by a Gaussian mixture model. The probability distribution of the image is used for noise suppression to alleviate the influence of the background regions having skin-like colors. Finally, the skin is segmented by graph cuts, with the regional parameter γ optimally selected to adapt to different images. Tests of the algorithm on many real world photographs show that the scheme accurately segments skin regions and is robust against illumination variations, individual skin variations, and cluttered backgrounds. 相似文献
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基于分层密度特征的文档图像检索 总被引:1,自引:0,他引:1
为克服基于版面重建的文档图像检索方法对图像质量要求高,且局限于部分文种,以及基于版面分割的文档图像检索方法受限于版面分割技术等问题,提出了一种基于二值文档图像分层密度特征的检索方法。该方法通过倾斜校正、去除黑边等预处理得到有效文本区域,提取有效文本区域的长宽比和分层密度特征,通过特征比对进行检索。实验表明:该方法对不同分辨率以及不同的输入设备具有自适应能力,对复杂版面和批注等噪声鲁棒性好,漏检率为2%,是一种简单有效的文档图像检索方法。 相似文献
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