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Landmark initialization defines the initial coordinates and the initial covariance of landmarks localization (also called interest points or features).

Landmark initialization consists of defining the initial coordinates and the initial covariance of landmarks (interest points).

Landmark initialization heuristics.

However, in the case of a monocular vision algorithm, the landmark initialization is not obvious.

For each landmark under initialization, each particle (a feature depth hypothesis) is projected into the image, matched and its probability is re-weighted.

If there is a lack of landmarks under initialization, we add aspiring new landmarks.

The new state vector includes new landmarks (whose initialization has just been performed) and previously used landmarks.

The third solution is to randomly select, at each training initialization time, landmarks among a very large set of nodes making it either infeasible or very costly for the CSP to blacklist all those nodes.

To avoid this attack, a solution is to randomly select, at each training step initialization time, landmarks among a very large set of nodes, which makes it either infeasible or very costly for CSP to learn and deploy an RTT forgery for each participating landmark.

When the initialization of a landmark is completed, the DSP processor returns the position and the uncertainty of possible new landmarks.

Various methods deal with the initialization issue, such as landmark localization using deformable part-based models [40], facial point detection using Gabor feature boosted classifiers [50] or boosted regression with Markov Random Fields [51].