Handwritten Chinese Radical Recognition Using Nonlinear Active Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Shape Model-Based Segmentation of Digital X-ray Images
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Color active shape models for tracking non-rigid objects
Pattern Recognition Letters - Special issue: Colour image processing and analysis
An Improved Active Shape Model for Face Alignment
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Multiscale 3D shape analysis using spherical wavelets
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
On-Line, incremental learning of a robust active shape model
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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Active Shape Models (ASM) are a successful image segmentation technique that is widely used by the image processing community. This technique is very appealing when the results of the segmentation are going to be used to perform some kind of classification, as it provides a mathematical model of the segmented contours. Nevertheless, little attention has been paid to the development of general local appearance models for small image training sets and most researchers have resorted to ad hoc solutions. In this paper we propose a heuristic approach to this problem. A general procedure for the use of heuristics to guide the ASM search algorithm and an implementation using machine learning classifiers is presented. This procedure is also extended to cope with multichannel images. Tests are carried out over small synthetic and real image datasets. The performance of this approach is compared to the most commonly used Mahalanobis appearance model and the simpler edge search strategy. The results show that the heuristic approach performs better than the other two procedures.