Statistical shape prior model is employed to construct the dynamics in probabilistic contour estimation.By applying principal component analysis,plausible shape samples are efficiently generated to predict contour sam...Statistical shape prior model is employed to construct the dynamics in probabilistic contour estimation.By applying principal component analysis,plausible shape samples are efficiently generated to predict contour samples.Based on the shape-dependent dynamics and probabilistic image model,a particle filter is used to estimate the contour with a specific shape.Compared with the deterministic approach with shape information,the proposed method is simple yet more effective in extracting contours from images with shape variations and occlusion.展开更多
By means of the chromatic polynomials, this paper provided a necessary and sufficient condition for the graph G being a mono-cycle graph(the Theorem 1), a first class hi-cycle graph and a second class bicycle graph...By means of the chromatic polynomials, this paper provided a necessary and sufficient condition for the graph G being a mono-cycle graph(the Theorem 1), a first class hi-cycle graph and a second class bicycle graph(the Theorem 2), respectively.展开更多
基金Supported by a grant from Hi-Tech 863 plan of People's Republic ofChina (No.2002AA311141)
文摘Statistical shape prior model is employed to construct the dynamics in probabilistic contour estimation.By applying principal component analysis,plausible shape samples are efficiently generated to predict contour samples.Based on the shape-dependent dynamics and probabilistic image model,a particle filter is used to estimate the contour with a specific shape.Compared with the deterministic approach with shape information,the proposed method is simple yet more effective in extracting contours from images with shape variations and occlusion.
基金Supported by the NNSF of China(10861009)Supported by the Ministry of Education Science and Technology Item of China(206156)
文摘By means of the chromatic polynomials, this paper provided a necessary and sufficient condition for the graph G being a mono-cycle graph(the Theorem 1), a first class hi-cycle graph and a second class bicycle graph(the Theorem 2), respectively.