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A Robust Active Appearance Models Search Algorithm

机译:强大的主动外观模型搜索算法

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With the aid of AAMs search algorithm, Active Appearance Models (AAMs) can represent non-rigid image objects with shape and texture variations well. However, the performance of the traditional AAMs search algorithm (TAAMS) is limited by its assumption that the error function is convex. Therefore, this paper proposes a robust AAMs search algorithm (RAAMS) which combines the multi-pose search (MS) for better pose matching and an estimation mechanism of parameter search direction (EPSD) for more accurate search direction. Moreover, a precaution mechanism of local minimum (PLM) is proposed to avoid the search trapped into the local minimum of the error function. Experimental results show that the proposed algorithm can significantly reduce 36.41% of shape error and 30.81% of texture error between the synthesized instance and target image.
机译:借助AAMS搜索算法,主动外观模型(AAMS)可以代表具有形状和纹理变化的非刚性图像对象。然而,传统的AAMS搜索算法(TAAMS)的性能受到其假设误差函数是凸的限制。因此,本文提出了一种坚固的AAMS搜索算法(RAAM),其将多姿势搜索(MS)组合以获得更好的姿势匹配和参数搜索方向(EPSD)的估计机制,以实现更准确的搜索方向。此外,提出了局部最小(PLM)的预防机制,以避免被困到误差函数的局部最小值中的搜索。实验结果表明,该算法在合成实例和目标图像之间建议的算法可以显着降低26.41%的形状误差和30.81%的纹理误差。

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