Abstract
Low level image processing is often used to detect and localise features such as edges and corners. It is also used to correlate or match small parts of one image with parts in another. Methods for doing this have been developed for some time, see (Canny, 1986; Michelli et al., 1989; Deriche, 1987; Faugeras, 1994; Hildreth and Marr, 1980; Marr, 1982; Nalwa and Binford, 1986; Torre and Poggio, 1986). However, the stochastic analysis of these algorithms have often been based upon poorly motivated stochastic models. In particular, the effects of image discretisation, interpolation and scale-space smoothing is often neglected or not analysed in detail.
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© 1997 Springer Science+Business Media Dordrecht
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Åström, K., Heyden, A. (1997). Stochastic Analysis of Image Acquisition and Scale-Space Smoothing. In: Sporring, J., Nielsen, M., Florack, L., Johansen, P. (eds) Gaussian Scale-Space Theory. Computational Imaging and Vision, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8802-7_9
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DOI: https://doi.org/10.1007/978-94-015-8802-7_9
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