A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A note on the least squares fitting of ellipses
Pattern Recognition Letters
3D Hand Model Fitting for Virtual Keyboard System
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
Evaluation of Background Subtraction Algorithms with Post-Processing
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fitting multiple connected ellipses to an image silhouette hierarchically
IEEE Transactions on Image Processing
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In many image processing applications, the structures conveyed in the image contour can often be described by a set of connected ellipses. Previous fitting methods to align the connected ellipse structure with a contour, in general, lack a continuous solution space. In addition, the solution obtained often satisfies only a partial number of ellipses, leaving others with poor fits. In this paper, we address these two problems by presenting an iterative framework for fitting a 2D silhouette contour to a pre-specified connected ellipses structure with a very coarse initial guess. Under the proposed framework, we first improve the initial guess by modeling the silhouette region as a set of disconnected ellipses using mixture of Gaussian densities or the heuristic approaches. Then, an iterative method is applied in a similar fashion to the Iterative Closest Point (ICP) (Alshawa, 2007; Li and Griffiths, 2000; Besl and Mckay, 1992) algorithm. Each iteration contains two parts: first part is to assign all the contour points to the individual unconnected ellipses, which we refer to as the segmentation step and the second part is the non-linear least square approach that minimizes both the sum of square distances between the contour points and ellipse's edge as well as minimizing the ellipse's vertex pair (s) distances, which we refer to as the minimization step. We illustrate the effectiveness of our methods through experimental results on several images as well as applying the algorithm to a mini database of human upper-body images.