Simultaneous localization and object detection using an a-contrario approach
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
SIAM Journal on Imaging Sciences
An a-contrario approach for obstacle detection in assistance driving systems
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Parallel unsupervised Synthetic Aperture Radar image change detection on a graphics processing unit
International Journal of High Performance Computing Applications
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This paper presents a new method for unsupervised subpixel change detection using image series. The method is based on the definition of a probabilistic criterion capable of assessing the level of coherence of an image series relative to a reference classification with a finer resolution. In opposition to approaches based on an a priori model of the data, the model developed here is based on the rejection of a nonstructured model—called a-contrario model—by the observation of structured data. This coherence measure is the core of a stochastic algorithm which automatically selects the image subdomain representing the most likely changes. A theoretical analysis of this model is led to predict its performances, in particular regarding the contrast level of the image as well as the number of change pixels in the image. Numerical simulations are also presented that confirm the high robustness of the method and its capacity to detect changes impacting more than 25 percent of a considered pixel under average conditions. An application to land-cover change detection is then provided using time series of satellite images.