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ias_perception: ann | cognitive_perception | contracting_curve_density_algorithm | cop_collision_interface | cop_ros_plugins | cop_sr4_plugins | jlo | lo | zbar | zbar_barcode_reader_node | zbar_qt_ros

Package Summary

Documented

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.

ias_perception: ann | cognitive_perception | contracting_curve_density_algorithm | cop_collision_interface | cop_ros_plugins | cop_sr4_plugins | jlo | lo | zbar | zbar_barcode_reader_node | zbar_qt_ros

Package Summary

Documented

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.

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Contents

The developed system mainly consists of two functional parts, the CCD algorithm to fit the model curve in still images and the CCD tracker to track the model in the videos. We demonstrate algorithm’s working in various scenes using handheld camera and the cameras from the PR2 robot. Achieved results show that the CCD algorithm achieves robustness and sub-pixel accuracy even in the presence of clutter, partial occlusion, and changes of illumination as depicted in below imagery. composite.jpg composite-bottle.jpg composite-pen-holder.jpg

Wiki: contracting_curve_density_algorithm (last edited 2012-09-29 03:44:37 by Dejan Pangercic)