Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Intensity-augmented Ordinal Measure for Visual Correspondence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Bayesian loop for synergistic change detection and tracking
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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In this paper we propose a formalization of change detection as a Bayesian order-consistency test, based on the assumption that disturbance factors such as illumination changes and variations of camera parameters do not change the ordering between noiseless intensities within a neighborhood of pixels. The assumption of additive, zero-mean, i.i.d. gaussian noise allows for testing the composite order-consistency hypothesis by efficient computation of the marginal likelihood. Moreover, since the above formalization enables to incorporate changed/unchanged class priors seamlessly, we also propose a simple method to derive informative priors based on the calculation of marginal likelihoods at reduced resolution. Experimental results on challenging test sequences characterized by sudden and strong illumination changes prove the effectiveness of the proposed approach.