Title
Constraint fusion for recognition and localization of articulated objects
Abstract
This paper presents a method for localization and interpretation of modeled objects that is general enough to cover articulated and other types of constrained models. The flexibility between the components of the model is expressed as spatial constraints that are fused into the pose estimation during the interpretation process. The constraint fusion assists in obtaining a precise and stable pose of each of the object's components and in finding the correct interpretation. The proposed method can handle any constraint (including inequalities) between any number of different components of the model. The framework is based on Kalman filtering.
Year
DOI
Venue
1996
10.1007/BF00131146
International Journal of Computer Vision
Keywords
Field
DocType
Image Processing,Artificial Intelligence,Computer Vision,Computer Image,Correct Interpretation
Computer vision,Computer science,Interpretation Process,Fusion,Image processing,Pose,Kalman filter,Artificial intelligence,Articulated body pose estimation,Robotics
Journal
Volume
Issue
ISSN
19
1
0920-5691
Citations 
PageRank 
References 
15
3.71
13
Authors
2
Name
Order
Citations
PageRank
Yacov Hel-Or146140.74
Michael Werman21889210.03