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A new algorithm for illumination normaliza-tion and uneven background correction in images, and this model requires no training
data, no assumption on the light source, and no align-ment between different images for illumination normaliza-tion
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The 3-D Morphable Model was
introduced as a generative model to p redictthe appearances o f
an individual while using a statistical prior on shape and
texture allowin g its parameters to be estimated from single
image. Based on these new unde
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熟练掌握光照模型的基本原理，尤其要了解简单光照模型的计算方法，深刻领会Gouraud明暗处理和Phong明暗处理的原理和算法。-Master the basic principles of illumination model, in particular, to understand the simple illumination model calculation, a profound understanding of Gouraud shading and Phong shading p
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采用Phone光照模型并通过引入外部光源来凸显生成图像的真实感，给出了一种预先为体数据场计算法线并编码，根据编码后的法线和Phone光照模型生成三类明暗光照表的加速算法，-Phone illumination model adopted by the introduction of an external light source to highlight generate realistic images, gives a pre-calculated data field normal to
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由一个3D变形人脸模型取自动生成适应的训练样本。由统计视角，tailored训练数据保证了所有的数据变化且由任意的人脸属性丰富训练样本，例如，年龄或体重。更进一步，它可能自动适应到环境约束，例如，来自于监控摄像机的照明或视角约束。我们使用裁剪的（tailor）图象训练一个新的Viola Jones的adaboost 目标检测框架的多核实现。这个新的实现不仅快速的，而且多特征通道的使用成为可能，例如，在训练期间的颜色特征。在我们实验中，我们训练7个依赖视角的人脸检测子并在Face Detectio
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