Title
Three dimensional model-based tracking using texture learning and matching
Abstract
In post-production, a traditional method for creating some special effects is named “rotoscopy”. This technique is used in animation and consists of segmenting and modifying a video sequence by hand and for every frame (i.e., adding an animated synthetic character to a real scene). Our method makes this process automatic by tracking a rigid object whose geometry is known. This new approach is based upon a two-steps process: One or several “keyframes” are used in a preliminary interactive calibration session, so that a 3D model of this object is positioned correctly on these images (its projection fits to the object in the image). We use this match to texture the 3D model. Then, a 3D predictor gives a position of the object model in the next image and the fine tuning of this position is obtained by simply minimizing the error between the textured model in this position and the real image of the object. This minimization is performed with respect to the six degree of freedom (DOF) of the model position (three translation parameters and three rotation ones). This procedure is iterated at each frame. Test sequences show how robust this method is.
Year
DOI
Venue
2000
10.1016/S0167-8655(00)00067-2
Pattern Recognition Letters
Keywords
Field
DocType
model-based tracking,dynamic prediction,texture matching,simulated annealing,degree of freedom,object model,three dimensional
Simulated annealing,Computer vision,Degrees of freedom (statistics),Pattern recognition,Computer science,Object model,Minification,Artificial intelligence,Animation,Real image,Iterated function,Calibration
Journal
Volume
Issue
ISSN
21
13-14
Pattern Recognition Letters
Citations 
PageRank 
References 
6
0.92
6
Authors
2
Name
Order
Citations
PageRank
Philippe Gérard160.92
André Gagalowicz226066.69