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In a comparison with other statistical approaches, the algorithm performs equally well for larger volumes and even shows improvements for small volumes and SBRs.
Unlike previous approaches, the algorithm does not rely upon range information and yet is able to handle complex environments irrespective of lighting conditions, reflections, wall or door color, or the relative orientation between the camera and the door.
As can be seen, the value of minimum μ in the last iteration is better for the case of PCE-based approach, since the algorithm examines some of the local minimum points together through the global minimum while in the other approaches the algorithm examines just one point.
The Mersenne Twister (Matsumoto and Nishimura 1998) is an example, at least until the number of random draws approaches the algorithm's period of 219937-1.
Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image.
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It can be observed that the performance of the proposed online algorithm approaches the proposed offline algorithm and especially in the low energy arrival rate region.
By using a Bayesian network based approach the POBN algorithm is able to identify both linear and nonlinear relationships.
In addition to the theoretical foundations of both approaches, the algorithms have been implemented.
Although each algorithm approaches the problem differently, all either explicitly or implicitly use the MAF information to weight variants.
In the enumeration approach, the algorithm tries all possibilities.
To substantiate theoretical results of the proposed approach, the algorithm is tested in real time Xilinx system generator (XSG).
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Justyna Jupowicz-Kozak
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