ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Comparing feature-based metrics for facial dynamics analysis
Proceedings of the SSPNET 2nd International Symposium on Facial Analysis and Animation
Deformable object modelling and matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Automatic part selection for groupwise registration
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Semi-supervised facial landmark annotation
Computer Vision and Image Understanding
Visualization of time-series data in parameter space for understanding facial dynamics
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Elastic image registration using hierarchical spatially based mean shift
Computers in Biology and Medicine
Facial expression recognition in dynamic sequences: An integrated approach
Pattern Recognition
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Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically, they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective function, representation of deformation field, and optimization methods. Given the complexity of the task, the final accuracy is significantly affected by the choices made for each component. Here, we present a groupwise registration algorithm which can take advantage of the statistics of both the image intensities and the range of shapes across the group to achieve accurate matching. By testing on large sets of images (in both 2D and 3D), we explore the effects of using different image representations and different statistical shape constraints. We demonstrate that careful choice of such representations can lead to significant improvements in overall performance.