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It may include some similarly colored background areas that distract the tracking process.
Background areas are iteratively filled by selecting the most appropriate (smooth) candidate blocks.
The results indicate that the McFIS captures more background areas compared to the conventional LTR frame.
This method explores the properties of the background areas, rather than the target objects.
where BG and FG represent the background areas and foreground objects, respectively.
In this paper, we proposed a background subtraction method that utilized structural similarity, which was robust against various background areas.
In the B sequence, the proposed method successfully detected objects that were similar to the background areas.
Spatial similarity is an effective way of eliminating shadows and detecting objects similar to the background areas.
Through the edge-based sampling strategy as shown in Fig. 3c, the noises from background areas are greatly reduced.
The main difficulties of background subtraction include various background changes, shadows, and objects similar in color to background areas.
If the proportion of ventricle in the striatal and background areas is balanced, then quantification is not affected.
Related(20)
background orientations
background streams
background reaches
background spheres
ground areas
background surfaces
context areas
background ranges
background squares
knowledge areas
background zones
origin areas
background borders
background stages
background opportunities
colour areas
background fields
background zone
backward areas
of the substantive areas
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