Author:

Keywords:

Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, AAM, ASM, model fusion, statistical model, segmentation, CARDIAC MR, SEGMENTATION, EFFICIENT, Algorithms, Artificial Intelligence, Cluster Analysis, Computer Simulation, Face, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Models, Statistical, Pattern Recognition, Automated, Subtraction Technique, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering, Artificial Intelligence & Image Processing, 4603 Computer vision and multimedia computation, 4611 Machine learning

Abstract:

This paper presents a framework for weighted fusion of several Active Shape and Active Appearance Models. The approach is based on the eigenspace fusion method proposed by Hall et al., which has been extended to fuse more than two weighted eigenspaces using unbiased mean and covariance matrix estimates. To evaluate the performance of fusion, a comparative assessment on segmentation precision as well as facial verification tests are performed using the AR, EQUINOX, and XM2VTS databases. Based on the results, it is concluded that the fusion is useful when the model needs to be updated online or when the original observations are absent.