A functional database - download pdf or read online

By Trinder P.

ISBN-10: 0902928619

ISBN-13: 9780902928619

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3. This algorithm was first introduced to the vision community by Isard and Blake [183] (called the CONDENSATION algorithm) to deal with a contour tracking problem. InitiaMze a sample set So = {^o , l ) } / = i according to prior distribution p{do). Fort = 1,2,... , J Resample iS(-i = { 6 } „ p " J ( „ i } to obtain a new sample {81_'^,\). Predict the sample by drawing u)^ ^ far ut and computing B^ = ft(S,_J,u, ). Compute the transformed image z"'' = T{yi; 9t}. 3. The general particle filter algorithm.

Factorization under uncertainty was considered in [175, 182]. The second difficulty is often resolved by imposing a subspace constraint on the face model. Bregler et al. [176] proposed to regularize the nonrigid face model by using the linear constrain. It was shown that factorization can be still be obtained. Brand [175] considered such factorization under uncertainty. Xiao et al. [228] discovered a closed form solution to nonrigid shape and motion recovery. Interpolation from a sparse set to a dense depth map is always a difficult task.

Compute the transformed image z"'' = T{yi; 9t}. 3. The general particle filter algorithm. Often in time series, we are interested in deriving the best estimate 9t given the observations up to now. i). j). ) is usually dependent on the application. We here focus on the extentions used in the vision literature. In the ICoNDENSATiON algorithm [184] which fiises low-level and high-level visual cues in the conventional CONDENSATION algorithm [183], the proposal distribution, a fixed Gaussian distribution for low-level color cue, is used to predict the particle configurations, then the posterior distribution of the highlevel shape cue is approximated using SIS.

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A functional database by Trinder P.

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