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New robust image operators and applications in automatic facial feature analysis

机译:新的强大图像运营商和自动面部特征分析中的应用

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This paper discusses a collection of image operators we originally developed for automatic analysis of face images, but they can also be applied to many other image domains. Most of these are new operators, a few are enhanced variants: two region segmentation algorithms (edge-based and intensity-based), two feature detectors (a hybrid morphological-Laplacian, and an oriented morphological edge detector), a thin edge detector by morphological gradient with statistical thresholding, and a nonlinear smoothing filter (micro-clustering). These new operators were designed with specific criteria for maximum efficiency in automatic image analysis. Morphological, statistical, linear and nonlinear operators were extensively tested and combined to get the desired properties.
机译:本文讨论了我们最初开发用于自动分析面部图像的图像运营商的集合,但它们也可以应用于许多其他图像域。其中大多数是新的运算符,少数是增强的变体:两个区域分割算法(基于边缘和强度为基础),两个特征探测器(混合形态学 - 拉普拉斯和面向定向的形态边缘检测器),薄边缘检测器具有统计阈值的形态梯度,以及非线性平滑过滤器(微聚类)。这些新操作员设计具有特定标准,可用于最大效率在自动图像分析中。广泛测试形态学,统计,线性和非线性算子并组合以获得所需的性质。

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