Wavelet-Based Independent Component Analysis For Statistical Shape Modeling

Zewail, Rami; Elsafi, Ahmed; Durdle, N.G.

doi:10.1109/ccece.2007.299

Cited by 2 publications

(1 citation statement)

References 12 publications

(13 reference statements)

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“…In [37], 2D images of the cardiac ventricle were used to train an Active Appearance Model based on Independent Component Analysis (ICA) [19]. Other applications of ICA for statistical shape models are presented in [34,42]. The Orthomax method, where the PCA basis is determined first and then rotated such that it has a "simple" structure, is used in [33].…”

Section: Deformation Model Variantsmentioning

confidence: 99%

Linear Shape Deformation Models with Local Support Using Graph-Based Structured Matrix Factorisation

Bernard

Gemmar

Hutter

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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α = +[ PCA (global support) Our (local support) PCA (global support) Our (local support) PCA (global support)Our (local support)Global support factors of PCA lead to implausible body shapes, whereas the local support factors of our method give more realistic results. See our accompanying video for animated results. AbstractRepresenting 3D shape deformations by highdimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. Based on matrix factorisation with sparsity and graph-based regularisation terms, we present a method to obtain deformation factors with local support. The benefits include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation and sparse reconstruction, whereas for body shapes our method gives more realistic deformations. 0 c 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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