An Improved Active Shape Model for Face Alignment
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
An accurate active shape model for facial feature extraction
Pattern Recognition Letters
Robust non-frontal face alignment with edge based texture
Journal of Computer Science and Technology
Face decorating system based on improved active shape models
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Automatic segmentation of human tibial cartilage
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Automatic segmentation of human tibial cartilage
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Weighted active appearance models
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
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Active Shape Models (ASMs), a knowledge-based segmentation algorithm developed by Cootes and Taylor [1, 2], have become a standard and popular method for detectingstructures in medical images. In ASMs - and various comparable approaches - the model of the object's shape and of its gray-level variations is based the assumption of linear distributions. In this work, we explore a new way to model the gray-level appearance of the objects, using a k-nearest-neighbors (kNN) classifier and a set of selected features for each location and resolution of the Active Shape Model. The construction of the kNN classifier and the se-lection of features from training images is fully automatic. We compare our approach with the standard ASMs on synthetic data and in four medical segmentation tasks. In allcases, the new method produces significantly better results. (p