A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Wavelet Compression of Active Appearance Models
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Functional Data Analysis with R and MATLAB
Functional Data Analysis with R and MATLAB
Image coding using wavelet transform
IEEE Transactions on Image Processing
Guest Editorial: Generative model based vision
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
Facial feature localization based on an improved active shape model
Information Sciences: an International Journal
IEEE Transactions on Image Processing
Texture guided active appearance model propagation for prostate segmentation
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Facial expression feature selection based on rough set
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Generative face alignment through 2.5D active appearance models
Computer Vision and Image Understanding
Computer Vision and Image Understanding
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Statistical region-based registration methods such as the active appearance model (AAM) are used for establishing dense correspondences in images. At low resolution, images correspondences can be recovered reliably in real-time. However, as resolution increases this becomes infeasible due to excessive storage and computational requirements. We propose to reduce the dimensionality of the textural components by selecting a subset of basis functions from a larger dictionary, estimate regression splines and model only the coefficients of the retained basis functions. We demonstrate the use of two types of bases, namely wavelets and wedgelets. The former extends the previous work of Wolstenholme and Taylor where Haar wavelet coefficient subsets were applied. The latter introduces the wedgelet regression tree based on triangulated domains. The wavelet and wedgelet regression splines are functional descriptions of the intensity information and serve to (1) reduce noise and (2) produce a compact textural description. Dimensionality reduction by subsampling in the CDF 9-7 wavelet and wedgelet representations yield better results than 'standard' subsampling in the pixel domain. We show that the bi-orthogonal CDF 9-7 wavelet yields better results than the Haar wavelet. Further, we show that the inherent frequency separation in wavelets allows for cost-free band-pass filtering, e.g. edge-emphasis, and that this edge enhancement provide better results in terms of segmentation accuracy. Wedgelet representation are superior to wavelet representations at high dimensionality-reduction rates. At low reduction rates an edge enhanced wavelet representation provides better segmentation accuracy than the full standard AAM model.