Automatic lip contour extraction using pixel-based segmentation and piece-wise polynomial fitting

Sukesh Das, Salam Nandakishor, D. Pati
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引用次数: 1

Abstract

This work presents automatic lip contour extraction using pixel-based segmentation and piece-wise polynomial fitting. In the first stage, the region of interest (ROI) i.e. mouth region is extracted by binary classification based on color ratio thresholding followed by centrally located large connected region detection. k-means clustering is applied to green plane to get upper lip area. The lower lip area is obtained by using binary k-means clustering to the weighted plane. The combined lip area is further processed to detect the centrally located big connected region. Robert filtering followed by similar neighbour traversing are employed to estimate the lip contour. A smoothed upper and lower contours are obtained by varying piece-wise polynomial fitting. Experimental results performed on standard GRID database show that the scheme performs well even under the influence of illumination and clothing effects.
基于像素分割和分段多项式拟合的唇形轮廓自动提取
这项工作提出了自动唇轮廓提取使用基于像素的分割和分段多项式拟合。第一阶段,通过基于颜色比阈值的二值分类提取感兴趣区域(ROI),即口腔区域,然后进行集中定位的大连通区域检测。对绿色平面进行K-means聚类,得到上唇面积。通过对加权平面进行二值k-means聚类,得到下唇区域。对组合唇区进行进一步处理,检测出位于中心的大连通区域。采用罗伯特滤波和相似邻域遍历来估计唇形轮廓。通过变化分段多项式拟合得到光滑的上下轮廓。在标准GRID数据库上进行的实验结果表明,该方案在光照和服装效果的影响下仍具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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