Sentence examples for map by minimizing from inspiring English sources

Typically, these algorithms solve for the disparity map by minimizing a pre-defined global cost function.

Global approaches tackle such problems by incorporating regularization such as explicit smoothness assumption and estimate the dense disparity map by minimizing an energy function.

DAGGER generates a consensus map by minimizing the "error" between the consensus map and the linkage maps.

We determined the optimal amount of smoothing for each map by minimizing the Akaike's Information Criterion (AIC).

We used a loess smooth, which adapts to changes in population density (Hastie and Tibshirani 1990) and determined the optimal amount of data for the smooth, or span size, for each map by minimizing the Akaike's Information Criterion AICCriterion AIC

In the framework of global approach, the dense stereo matching problem is formulated in terms of energy minimization where the objective is to estimate the disparity map d by minimizing the following energy function: E(d)=E_{D}(d)+E_{P}(d), (1).

The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function.

This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur) and obtaining a MAP estimate by minimizing a cost function including an appropriate prior.

Finally, an iterative two-phase algorithm is proposed to estimate the dense disparity map where in phase one, sparse representation of disparities are inferred from the trained sparse autoencoder, and IGMRF parameters are computed, keeping the disparity map fixed and in phase two, the disparity map is refined by minimizing the energy function using graph cuts, with other parameters fixed.

In phase one, sparseness is inferred using the learned weights of the sparse autoencoder, and IGMRF parameters are computed based on the current estimate of disparity map, while in the second phase, the disparity map is refined by minimizing the energy function with other parameters fixed.

We have presented an iterative two-phase algorithm for disparity estimation where in phase one, the disparity map is estimated by minimizing our energy function using graph cuts and in phase two, the IGMRF parameters and sparse representation of disparity maps are obtained.