Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes
International Journal of Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Automatic Landmark Identification Using a New Method of Non-rigid Correspondence
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
A bootstrapping algorithm for learning linear models of object classes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Locating Salient Facial Features Using Image Invariants
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Journal of Cognitive Neuroscience
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In order to build a statistical model of appearance we require a set of images, each with a consistent set of landmarks. We address the problem of automatically placing a set of landmarks to define the correspondences across an image set. We can estimate correspondences between any pair of images by locating salient points on one and finding their corresponding position in the second. However, we wish to determine a globally consistent set of correspondences across all the images. We present an iterative scheme in which these pair-wise correspondences are used to determine a global correspondence across the entire set. We show results on several training sets, and demonstrate that an Appearance Model trained on the correspondences can be of higher quality than one built from hand marked images.