Graffiti Detection Using a Time-Of-Flight Camera
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Bayesian Order-Consistency Testing with Class Priors Derivation for Robust Change Detection
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Matching points in poor edge information images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Non-linear parametric Bayesian regression for robust background subtraction
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Foreground-background separation on GPU using order based approaches
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Second-order polynomial models for background subtraction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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Determining the correspondence of image patches is one of the most important problems in Computer Vision. When the intensity space is variant due to several factors such as the camera gain or gamma correction, one needs methods that are robust to such transformations. While the most common assumption is that of a linear transformation, a more general assumption is that the change is monotonic. Therefore, methods have been developed previously that work on the rankings between different pixels as opposed to the intensities themselves. In this paper, we develop a new matching method that improves upon existing methods by using a combination of intensity and rank information. The method considers the difference in the intensities of the changed pixels in order to achieve greater robustness to Gaussian noise. Furthermore, only uncorrelated order changes are considered, which makes the method robust to changes in a single or a few pixels. These properties make the algorithm quite robust to different types of noise and other artifacts such as camera shake or image compression. Experiments illustrate the potential of the approach in several different applications such as change detection and feature matching.