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Now let "*" denote the fusion operator.
(IC1) states that, when IC is consistent, then the result of the fusion operator will also be consistent.
Figure4b shows the detected candidates using MLFD and Figure4c is the detection results of fusion operator, similarly, the fusion operator still generates lots of unnecessary segments.
This experiment mainly aims at drawing a comparison of detection results using the proposed detector, the fusion operator, wedgelets, beamlets[16], the unbiased detector raised by Steger[22].
The fusion operator has an unfavorable performance on detecting airstrips which have large widths using a smaller template size (13 × 15) as shown in Figure3c, and it results in large gaps at the "thin" roads when taking a larger template size (25 × 29), shown in Figure3d.
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The fusion operators in the literature can be divided in two classes: syntax-based fusion and model-based fusion.
In the formula above, ∘ stands for the corresponding fusion operator; the function fuses the information from two images as one.
Figure4d is the result by fusion operator with a larger template size, and it also miss many road segments using a larger template.
Comparison of linear feature detection results among fusion operator (the first row), wedgelet (the second row) and MLFD (the last row).
The complexity of a fusion operator (6) is (O |Lambda |^2)) in general, because it corresponds to the matrix-vector multiplication [43].
An IC majority fusion operator minimizes the level of total dissatisfaction (as introduced by Lin and Mendelzon (1996), whereas an IC arbitration operator aims at equally distributing the level of individual dissatisfaction among the agents.
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