Table 4: Residual error of different pairs of medical images
Method
Group
RANSAC MLESAC MGA SEA SEA+T FMEA FMEA+T
MG1 6.537 1.664 1.584 1.702 1.887 1.637 1.621
MG2 8.025 2.124 1.621 2.116 2.075 1.936 1.907
MG3 29.06 3.032 2.135 2.626 2.651 2.452 2.276
5 CONCLUSION
In this paper, we described a novel competitive
evolutionary agent-based approach to trifocal tensor
estimation, which employs a new competitive
strategy to control the breeding number of new
agents and reduce the chance of reproducing unfit
ones. It focuses on the reproduction behavior to
reduce the computation time, and produces results
commensurate with, or superior to, that of SEA. The
experimental results indicate that the proposed
method attains a high level of performance in terms
of accuracy and computational efficiency. It can
obtain an optimal (or near optimal) result in the
solution space and is robust to outliers, even when a
large number of outliers are involved.
By accurately estimating the trifocal tensor, it
will now be possible to generate 3D views of the
sequence of 2D images. This brings the authors
closer to their ultimate goal, the real time generation
of 3D views during a laparoscopic procedure in
order to enhance features, in particular obscured
features, to the surgeon. This requires that the
geometric data are estimated as fast and accurately
as possible. The novel finite multiple evolutionary
agent-based approach presented here allows us to do
this.
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MULTIPLE VIEW GEOMETRY ESTIMATION BASED ON FINITE-MULTIPLE EVOLUTIONARY AGENTS FOR
MEDICAL IMAGES
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