Illumination independent change detection for real world image sequences
Computer Vision, Graphics, and Image Processing
Statistical model-based change detection in moving video
Signal Processing
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
Motion Analysis for Image Sequence Coding
Motion Analysis for Image Sequence Coding
Statistical change detection with moments under time-varying illumination
IEEE Transactions on Image Processing
Proceedings of the 16th ACM international conference on Supporting group work
Non-linear parametric Bayesian regression for robust background subtraction
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Real-time pointing gesture recognition for an immersive environment
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A multiscale co-linearity statistic based approach to robust background modeling
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Perceptive user interface, a generic approach
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Imperceptible projection blanking for reliable segmentation within mixed reality applications
EGVE'05 Proceedings of the 11th Eurographics conference on Virtual Environments
| Hi-index | 0.00 |
This paper describes a newalgorithm for illumination-invariant change detection that combines a simple multiplicative illumination model with decision theoretic approaches to change detection. The core of our algorithm is a new statistical test for linear dependence (colinearity) of vectors observed in noise. This criterion can be employed for a significance test, but a considerable improvement of reliability for real-world image sequences is achieved if it is integrated into a Bayesian framework that exploits spatio-temporal contiguity and prior knowledge about shape and size of typical change detection masks. In the latter approach, an MRF-based prior model for the sought change masks can be applied successfully. With this approach, spurious spot-like decision errors can be almost fully eliminated.