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Additional info for Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part II
5. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995. 6. D. Boissonnat. Representation of objects by triangulating points in 3-d space. In Proceedings of ICPR, pages 830–832, 1982. 7. J. Dolan and R. Weiss. Perceptual grouping of curved lines. In Image Understanding Workshop, pages 1135–1145, 1989. 8. H. Edelsbrunner and E. P. M¨ ucke. Three-dimensional alpha shapes. ACM Transactions on Graphics, 13(1):43–72, 1994. 9. P. Fua and P. Sander. Segmenting unstructured 3d points into surfaces.
Different methods have been proposed. Most of those assume one of the following. There are certain particular values of the variables that represent well the data: cluster analysis. There exists special linear or polynomial combinations of the variables that yield simple relations: principal component analysis, discriminant function analysis, multi-dimensional scaling, canonical correlation, step-wise linear and nonlinear regression, projection pursuit. All these techniques are nicely presented in this textbook .
E. for any surface s s ˜ ˜ = 1. hη (X, X)dA( X) (2) We are able to measure the intensity I(x) at each point x. Our goal is to reconstruct both the radiance r and the shape s of the scene from a collection of images obtained with different focal settings. Suppose we have L images with different settings η1 , . . , ηL . We collect and organize . these images into an array I = [Iη1 , . . , IηL ]T , and do the same for the respective . T kernels h = [hη1 , . . , hηL ] . The right-hand side of equation (1) can also be interpreted as the synthetic image generated by a given surface s radiating energy with a given .