351 research outputs found
Characterizing Human Mobility Patterns in a Large Street Network
Previous studies demonstrated empirically that human mobility exhibits Levy
flight behaviour. However, our knowledge of the mechanisms governing this Levy
flight behaviour remains limited. Here we analyze over 72 000 people's moving
trajectories, obtained from 50 taxicabs during a six-month period in a large
street network, and illustrate that the human mobility pattern, or the Levy
flight behaviour, is mainly attributed to the underlying street network. In
other words, the goal-directed nature of human movement has little effect on
the overall traffic distribution. We further simulate the mobility of a large
number of random walkers, and find that (1) the simulated random walkers can
reproduce the same human mobility pattern, and (2) the simulated mobility rate
of the random walkers correlates pretty well (an R square up to 0.87) with the
observed human mobility rate.Comment: 13 figures, 17 page
Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning
Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL
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