Abstract
We propose an approach to image comparison that accounts for deformations and lighting changes in the object being viewed. We address this problem by defining a Riemannian manifold of the space of all images, in which the geodesic distance between two points represents the distance between two images. In order for this manifold to capture the effects of lighting and deformation, we define a local image metric that measures deformations and intensity changes. In particular, the component of our metric that handles intensity variations is designed to penalize changes less if they are likely to be due to lighting variation. We provide some theoretical properties of the resulting geodesic distances. We also show the potential value of this new local metric, by incorporating it into an optical flow framework and then showing that this can be used for face recognition. Finally, we show that the lighting-insensitive cost for intensities that we introduce allows us to compute an approximation to geodesic distances for this component of our metric very efficiently, by working in the wavelet domain.
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Jacobs, D., Jorstad, A., Trouvé, A. (2013). Deformations and Lighting. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_9
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DOI: https://doi.org/10.1007/978-1-4471-5195-1_9
Publisher Name: Springer, London
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