title: 3D point correspondence by minimum description length with 2DPCA.
abstract: Finding point correspondences plays an important role in automatically building statistical shape models from a training set of 3D surfaces. Davies et al. assumed the projected coefficients have a multivariate Gaussian distributions and derived an objective function for the point correspondence problem that uses minimum description length to balance the training errors and generalization ability. Recently, two-dimensional principal component analysis has been shown to achieve better performance than PCA in face recognition. Motivated by the better performance of 2DPCA, we generalize the MDL-based objective function to 2DPCA in this paper. We propose a gradient descent approach to minimize the objective function. We evaluate the generalization abilities of the proposed and original methods in terms of reconstruction errors. From our experimental results on different sets of 3D shapes of different human body organs, the proposed method performs significantly better than the original method.
authors: Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA. jhchen@cs.washington.edu
authors: Jiun-Hung
authors: JH
authors: Chen
authors: Linda G
authors: LG
authors: Shapiro
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014781:Viscera / Q000033:anatomy & histology*