Nonrigid Structure-From-Motion From 2-D Images Using Markov Chain Monte Carlo ABSTRACT:In this paper we present a new method for simultaneously determining 3-D shape and motion of a nonrigid object from uncalibrated 2-D images without assuming the distribution characteristics. A nonrigid motion can be treated as a combination of a rigid rotation and a nonrigid deformation. To seek accurate recovery of deformable structures, we estimate the probability distribution function of the corresponding features through random sampling, incorporating an established probabilistic model. The fitting between the observation and the projection of the estimated 3-D structure will be evaluated using a Markov chain Monte Carlo based expectation maximization algorithm. Applications of the proposed method to both synthetic and real image sequences are demonstrated with promising results. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Multimodal Video Indexing and Retrieval Using Directed Information Parallel Lasso for Large-Scale Video Concept Detection
