Active shape models—their training and application
Computer Vision and Image Understanding
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Affine-Invariant Geometric Shape Priors for Region-Based Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Weighted Fusion of Multiple Statistical Models of Shape and Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Shape Models with Invariant Optimal Features: Application to Facial Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Search strategies for shape regularized active contour
Computer Vision and Image Understanding
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Coarse-to-fine statistical shape model by Bayesian inference
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Active Shape Modeling with Electric Flows
IEEE Transactions on Visualization and Computer Graphics
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A regularized correntropy framework for robust pattern recognition
Neural Computation
An integrated model for accurate shape alignment
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Active shape model (ASM), as a method for extracting and representing object shapes, has received considerable attention in recent years. In ASM, a shape is represented statistically by a set of well-defined landmark points and its variations are modeled by the principal component analysis (PCA). However, we find that both PCA and Procrustes analysis are sensitive to noise, and there is a linear relationship between alignment error and magnitude of noise, which leads parameter estimation to be ill-posed. In this paper, we present a sparse ASM based on l1-minimization for shape alignment, which can automatically select an effective group of principal components to represent a given shape. A noisy item is introduced to both shape parameter and pose parameter (scale, translation, and rotation), and the parameter estimation is solved by the l1-minimization framework. The estimation of these two kinds of parameters is independent and robust to local noise. Experiments on face dataset validate robustness and effectiveness of the proposed technique.