Surface estimation and tracking using sequential MCMC methods for video based rendering
Bowen, Adam, Mullins, Andrew, Wilson, Roland and Rajpoot, Nasir (2007) Surface estimation and tracking using sequential MCMC methods for video based rendering. In: IEEE International Conference on Image Processing (ICIP 2007), San Antonio, TX, SEP 16-19, 2007. Published in: 2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7 pp. 1125-1128.Full text not available from this repository.
Video based rendering algorithms attempt to render videos of a scene from an arbitrary viewpoint, given a set of input video sequences taken from several fixed viewpoints. These algorithms require either a dense camera array or some knowledge of scene structure. By applying sequential Markov Chain Monte Carlo (MCMC) methods, we show it is possible to estimate the surfaces visible within a scene, and track them over time, in an efficient manner. Initially, a particle filter is applied across image scale to estimate the surfaces present in a scene at a fixed point in time. Following this, surfaces are tracked over time using a particle filter which takes advantage of both frame-to-frame dependancies, and a hierarchical surface model derived from a multiresolution Gaussian mixture model analysis of the surface data. This time-varying surface model, and the images, are the input for a rendering algorithm which uses a fuzzy z-buffer and projective texturing to generate reconstructions.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||T Technology > TR Photography|
|Series Name:||IEEE International Conference on Image Processing (ICIP)|
|Journal or Publication Title:||2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7|
|Number of Pages:||4|
|Page Range:||pp. 1125-1128|
|Title of Event:||IEEE International Conference on Image Processing (ICIP 2007)|
|Location of Event:||San Antonio, TX|
|Date(s) of Event:||SEP 16-19, 2007|
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