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This paper presents a motion segmentation algorithm for detecting people moving in indoor environments.
Then, the identification of the robots is obtained by comparing the components of the linear velocity estimated by the motion segmentation algorithm and received from the odometers.
Based on a formulation of the clustering task as an optimization problem using a multi-labeled Markov Random Field, we develop a semi-supervised motion segmentation algorithm by setting up a framework for incorporating prior knowledge into the segmentation algorithm.
In [5], a motion segmentation algorithm is proposed for extracting foreground objects with a PTZ camera.
This paper presents an adaptive motion segmentation algorithm utilizing spatiotemporal information of three most recent frames.
Tong et al. [39] extracted local motion in the consecutive frames and determined the object area using the motion segmentation algorithm.
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Other approaches consider the body in its entirety [ 34], for instance, demonstrated an online motion-segmentation algorithm of whole-body motion based on hidden Markov models.
Zucchelli et al. [7] detected planes from stereo images using a motion-based segmentation algorithm.
Figure 12 Motion segmentation with the algorithm (b) and the RAE algorithm (c).
Table 1 Execution times for the proposed segmentation algorithm Motion detection 469 μs → (ACE16k) Edge detection 34874 μs → 6096 μs (ACE16k) + 28778 μs (DSP) Object detection 2424 μs → (ACE16K) Total 37767 μs.
Optical coherence angiography (OCA) [ 15] is a set of hardware and software methods for enhanced visualization of retinal and choroidal vasculature; OCA includes motion compensation and segmentation algorithms and is based on Doppler OCT [ 15] and intensity-based segmentation [ 16, 17].
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motion tracking algorithm
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motion reduction algorithm
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motion compensation algorithm
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motion coordination algorithm
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