Multiple view geometry in computer vision
Multiple view geometry in computer vision
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Uncertain Projective Geometry: Statistical Reasoning For Polyhedral Object Reconstruction (Lecture Notes in Computer Science)
Determining an initial image pair for fixing the scale of a 3d reconstruction from an image sequence
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Estimation of geometric entities and operators from uncertain data
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
| Hi-index | 0.00 |
Recursive estimation or Kalman filtering usually relies on explicit model functions, that directly and explicitly describe the effect of the parameters on the observations. However, many problems in computer vision, including all those resulting in homogeneous equation systems, are easier described using implicit constraints between the observations and the parameters. By implicit we mean, that the constraints are given by equations, that are not easily solvable for the observation vector. We present a framework, that allows to incorporate such implicit constraints as measurement equations into a Kalman filter. The algorithm may be used as a black-box, simplifying the process of specifying suitable measurement equations for many problems. As a byproduct, the possibility of specifying model equations non-explicitly, some non-linearities may be avoided and better results can be achieved for certain problems.