EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Alignment Using Distributions of Local Geometric Properties
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Alignment by Maximization of Mutual Information
Alignment by Maximization of Mutual Information
Robust Multi-Sensor Image Alignment
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper proposes an image matching method that is robust to illumination variation and affine distortion. Our idea is to do image matching through establishing an imaging function that describes the functional relationship relating intensity values between two images. Similar methodology has been proposed by Viola [11] and Lai & Fang [6]. Viola proposed to do image matching through establishment of an imaging function based on a consistency principle. Lai & Fang proposed a parametric form of the imaging function. In cases where the illumination variation is not globally uniform and the parametric form of imaging function is not obvious, one needs to have a more robust method. Our method aims to take care of spatially non-uniform illumination variation and affine distortion. Central to our method is the proposal of a localized consistency principle, implemented through a non-parametric way of estimating the imaging function. The estimation is effected through optimizing a similarity measure that is robust under spatially non-uniform illumination variation and affine distortion. Experimental results are presented from both synthetic and real data. Encouraging results were obtained.