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Object tracking is a challenging task in computer vision due to the constant changes of object appearance and location.
We considered two termination criteria: the maximum number of iterations and reaching the changes of object function below a predefined threshold.
Typical outliers include the dynamic background samples and the sudden and great changes of object appearance, which usually cannot be well described with the current background model.
By evaluating both the distance between objects and the changes of object size in an image, Yilmaz et al. [14] proposed a contour-based tracking to cope with the occlusion problem.
In this research, we develop a new approach called the collaborative behavior-based approach, in which behaviors of robots and behaviors of human users, as well as the changes of object states caused by the behaviors, are taken into consideration integratedly in processing natural language user instructions.
When compensating for changes of object orientation, similar overall patterns of performance to those observed in vision have been found in the haptic modality [2] [5].
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Accordingly, in this functional magnetic resonance imaging (fMRI) study we asked participants to detect color changes of objects placed within natural scenes that were presented either with binocular and monocular cues (bmC) or with monocular cues only (mC).
To detect the change of object position, areas of interest are often set several times larger than the size of the object.
However, to effectively discriminate the background sample from the object is still a challenging task due to the change of object appearance.
Experimental results show that if the flexural stiffness gets smaller, sensing the change of object deformation gets easier and the force measurement precision gets higher.
To account for the change of object appearance, almost all the trackers mentioned above explore an adaptive appearance model for the on-line sample updating [5] or learning [16,17].
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