Abstract-This paper investigates an extended and optimized implementation of the state-of-the-art local curve fitting algorithm named Contracting Curve Density (CCD) algorithm, originally developed by Hanek et al. In particular, we investigate its application in the field of personal robotics for the tasks such as the mobile manipulation which requires a segmentation of objects in clutter and the tracking of them. The developed system mainly consists of the 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 Personal Robot 2 (PR2). 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.

Abstract. The point-set registration technology has been widely applied in the field of medical image registration. This paper proposed a characteristic statistic based femoral contour point-set registration algorithm, in order to overcome the flaw of getting into local extrema and of poor global performance in the iterative optimization process in existing registration algorithms. First, it uses the Gaussian mixture model to represent the correspondence of points between two point-sets, and selects the global parameter associated with the objective function as the characteristic statistical items. Then, it leverages the statistical variation in the optimization process to guide the optimal search, yielding the global optimal solution of statistical items, and realizing the registration of two point-sets. In the end, this algorithm is applied to solve the registration problem of femoral contour characteristic point-set. The experimental results reveal that the proposed registration method performs more effective, faster, and more accurate compared with Coherent Point Drift (CPD) algorithm.