contracting_curve_density_algorithm Documentation


An extended and optimized implementation of the state-of-theart local curve fitting algorithm named Contracting Curve Density (CCD) algorithm, originally developed by Hanek et al. The CCD algorithm can be best described as follows. Given one or multiple images as input data and a parametric curve model with a priori distribution of model parameters, through curve-fitting process, we estimate the model parameters which determine the approximation of the posterior distribution in order to make the curve models best matching the image data. In order to improve the stability, accuracy and robustness over the original implementation we introduce the following improvements. Firstly, we use the logistic sigmoid function instead of a Gaussian error function which renders a curve-fitting problem as a Gaussian logistic regression problem known in the field of pattern recognition. Secondly, a quadratic or a cubic B-spline curve is used to model the parametric curve to avoid the Runge phenomenon without increasing the degree of the B-spline. Thirdly, the system supports both planar affine (6-DOF) and three-dimensional affine (8-DOF) shapespace. The latter affine space can avoid curve mismatching caused by major viewpoint changes. Lastly, in order to avoid manual intervention by the user, the developed system also supports robust global initial curve initialization modules based on both keypoint feature matching and back-projections from the 3D point clouds.

ccd is ...


Author(s): Shulei Zhu, Dejan Pangercic
autogenerated on Mon Oct 6 2014 10:42:03