KJB

#include <list>
#include <vector>
#include <edge_cpp/line_segment.h>
#include <edge_cpp/edge.h>
#include <edge_cpp/line_segment_set.h>
#include <likelihood_cpp/model_edge.h>
#include <likelihood_cpp/edge_likelihood_util.h>
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Namespaces  
kjb  
Classes and functions for dealing with trajectory files.  
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Apart from the remix rights granted under this license, nothing in this license impairs or restricts the author's moral rights. This file contains functions used to compute the likelihood of a set of deteceted edges given a set of backprojected model edges (ie, the backprojection of the model gives a high likelihood if it explains the detected edges well). We treat each edge point independently here. The likelihood value is computed in the following way: 1) We first need to find a correspondence between model edge points and image edge points. We match each model point to the closest edge point along the direction of the edge gradient (there is no match is the closest match is too far away). It follows that each edge point can be explained by several model point (ie edge point e1 could be the closest edge point to model point m1 and also the closest edge point to model point m2). 2) For each edge point we compute a penalty. This is a constant value if the edge point is not explained by any model point (we explain these points as noise). Otherwise, if an edge point matches one and only one model point, the penalty is proportional (with a gaussian falloff) to their distance along the edge gradient, and to the difference in orientation between model edge and detected edge. If an edge point is matched to multiple model points, the penalty is the weighted average of the penalty for each possible match. Last, we penalize model points that are not matched to any edge point (we explain this as edge points missed by the edge detector)
For more details please see Figure 5 and 6 in: http://vision.cs.arizona.edu/~schlecht/research/furniture/papers/schlecht2009b.pdf