Most existing superpixel algorithms ignore the spatial structure and regularity properties, which result in undesirable sizes and location relationships for the subsequent processing. In this paper, we introduce a new method to generate the regularity preserved superpixels. Starting from the lattice seeds, our method relocates them to the pixel with locally maximal edge magnitudes and treats them as the superpixel junctions. Then, the shortest path algorithm is employed to find the local optimal boundary connecting each adjacent junction pair. Thanks to the local constraints, our method obtains homogeneous superpixels with adjacency in lowly textured and uniform regions and simultaneously preserves the boundary adherence in the high contrast contents. Our method preserves the regularity property without significantly sacrificing the segmentation accuracy. Moreover, we extend this regular constraint for generating the supervoxels. Our method obtains the regular supervoxels, which preserves the structural relation on both spatial and temporal spaces of the video. Quantitative and qualitative experimental results on benchmark datasets demonstrate that our simple but effective method outperforms the existing regular superpixel methods.

Most existing super pixel algorithms ignore the topology and regularities, which results in undesirable sizes and location relationships for subsequent processing. In this paper, we introduce a new method to compute the regular super pixels while preserving the topology. Start from regular seeds, our method relocates them to the pixel with locally maximal edge magnitudes. Then, we find the local optimal path between each relocated seed and its four neighbors using Dijkstra algorithm. Thanks to the local constraints, our method obtains homogeneous super pixels with explicit adjacency in low-texture and uniform regions and, simultaneously, maintains the edge cues in the high contrast and salient contents. Quantitative and qualitative experimental results on Berkeley Segmentation Database Benchmark demonstrate that our proposed algorithm outperforms the existing regular super pixel methods.

In this paper, we analyze the way in which international Facebook users who had recently moved to the United States used different languages to selectively self-disclose to their old (native-language) and new (English-speaking) social circles. We found significantly more intimate self-disclosure, covering a broader range of cognitive and emotional topics, in native-language status updates compared to updates in English. Self-disclosure was also more positive in English. These patterns support our hypotheses that users exploit language barriers to serve different self-presentational goals for different social circles and generate implications for SNS privacy control.