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A background object can be moved.
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Therefore, they have bi-directional disparities, such that when the left image is taken as the reference, then the resulting disparities for background objects can be expected to be positive and for foreground objects as negative.
Object can be detected more accurately by subtracting background frame (generated from the background model) from the current frame.
If the proper threshold t is found, object can be identified from the background, shadow and the less informative pixels.
The object can be detected more accurately by subtracting the background frame (generated from the background model) from the current frame.
CNR indicates whether the reconstructed object can be clearly distinguished from the background.
Therefore, the server robot may mistake a target for background false positive objects and not all representations of the object can be shared among the robots.
Lifting an object can be problematic.
Determine if your object can be painted.
By using real object textures, computer-generated objects can be well matched to background frames and become hardly detectable as computer objects.
Secondly, there is no background and a limited number of simple objects; the background is the real world, and ergonomics impose that a few simple objects can be drawn at the same time.
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Justyna Jupowicz-Kozak
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