Abstract We propose a novel approach to simulate the illumination of augmented outdoor scene based on a legacy photograph. Unlike previous works which only take surface radiosity or lighting related prior information as the basis of illumination estimation, our method integrates both of these two items. By adopting spherical harmonics, we deduce a linear model with only six illumination parameters. The illumination of an outdoor scene is finally calculated by solving a linear least square problem with the color constraint of the sunlight and the skylight. A high quality environment map is then set up, leading to realistic rendering results. We also explore the problem of shadow casting between real and virtual objects without knowing the geometry of objects which cast shadows. An efficient method is proposed to project complex shadows (such as tree's shadows) on the ground of the real scene to the surface of the virtual object with texture mapping. Finally, we present an unified scheme for image composition of a real outdoor scene with virtual objects ensuring their illumination consistency and shadow consistency. Experiments demonstrate the effectiveness and flexibility of our method.

A single antenna GSM/EDGE receiver design with fractionally spaced (FS) channel estimation and shortening is presented using trellis based joint detection for co-channel interference cancellation. For the combination of joint trellis equalization and DFE, we propose FS prefiltering to shorten both the impulse responses of a desired user and a dominant co-channel interferer jointly, followed by an optional whitening filter of low order to mitigate the coloration induced by the shortening prefilter. For the whitening filter, an auto-regressive (AR) model is assumed. We compare two optimization criteria: the maximum shortening SNR (MSSNR) and a novel criterion related to the minimum mean square error (MMSE) criterion, henceforth called MMSEr, by simulating the resulting SSNRs, the BERs, and assessing their numerical stability. In addition, we compare fractional with symbol spaced prefiltering. We address the problem of detecting shifted users and show results in mixed modulation environments. The channel estimators based on joint least squares (JLS) and maximum likelihood (ML) are compared to the Cramer-Rao bound (CRB) for FS processing. Finally, computational issues are discussed