Hence, we calculate the distance dist i,j,l) between each candidate point (d sin θ i, cτ j ) and the path different line cτ - d sin θ = cD l as: dist ( i, j, l ) = 1 2 | c τ j - d sin θ i - c D l |. (6).
Based on the existing map-matching algorithm geometry, it shows that if the distance between the candidate points and the locus of points are closer, the greater the likelihood of the candidate point is the best match point.
Based on this, the algorithm stores only the first five candidate points, and the segments where the distance between the candidate points and the track points is the smallest in the database. .
V ' is the candidate point of the track point; T ' is the side represented by the shortest path between two adjacent candidate points.
In a race between a candidate and a non-candidate, the candidate wins every time.
For a certain facial landmark, Adaboost classifier outputs a response depicting the similarity between the representations of the candidate points compared to the learned training model.
From the other hand it is noticeable that, with constant value of D p threshold, the bigger is maximum distance between reference points (which is a result of value of radius R of the analysed neighbourhood see 1.2 for details) the bigger is tolerance field for real difference between reference points distance and candidate points distance.
In Gaussian noise, the likelihood function is expressed as the distance between the received signal and candidate points in the constellation (distance metric).
A kernel correlation analysis approach is proposed to find the detection likelihood by maximizing a similarity criterion between the target points and the candidate points.
