Active appearance models revisited
Active appearance models revisited
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DOI：
10.1023/b:visi.0000029666.37597.d3
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发表时间：
2004-11-01
影响因子：
19.5
通讯作者：
Baker, S
中科院分区：
文献类型：
--
作者：
Matthews, I;Baker, S
主动外观模型（AAM）以及与之密切相关的Morphable Models和Active Blob是特定视觉现象的生成模型。虽然在形状和外观上都是线性的，但总体而言，AAM在像素强度方面是非线性参数模型。将AAM拟合到图像包括最小化输入图像和最接近的模型实例之间的误差;即解决非线性优化问题。我们提出了一种有效的拟合算法的基础上的反合成图像对齐算法的AAM。我们表明，在拟合过程中的外观变化的影响，可以预先计算（“投影”）使用该算法，以及它如何可以扩展到包括一个全球形状规范化的翘曲，通常是一个2D的相似性变换。我们评估我们的算法，以确定其新颖的方面提高AAM拟合性能。
Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instance; i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed ("projected out") using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.