基于张量的AAM改进人脸模型拟合

2008 International Symposium on Ubiquitous Virtual Reality Pub Date : 2008-07-10 DOI:10.1109/ISUVR.2008.15

Daijin Kim, Hyung-Soo Lee

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引用次数: 0

摘要

本文提出了一种基于张量的主动外观模型(AAM)，提高了传统AAM的拟合性能。基于张量的AAM通过根据估计的输入图像变化索引模型张量来生成特定的AAM基向量。实验结果表明，基于张量的AAM比传统AAM显著降低了平均拟合误差。

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Improved Face Model Fitting Using Tensor-Based AAM

In this paper, we propose a tensor-based active appearance model (AAM) which improves the fitting performance of conventional AAM. Tensor-based AAM generates the specific AAM basis vectors by indexing the model tensor in terms of the estimated input image variations. Experimental results show that the proposed tensor-based AAM reduces the average fitting error than the conventional AAM significantly.

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2008 International Symposium on Ubiquitous Virtual Reality

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