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VCells:使用边缘加权质心 Voronoi 图的简单高效超像素。

VCells: simple and efficient superpixels using Edge-Weighted Centroidal Voronoi Tessellations.

机构信息

Department of Scientific Computing, Florida State University, Tallahassee, FL 532306-4120, USA.

出版信息

IEEE Trans Pattern Anal Mach Intell. 2012 Jun;34(6):1241-7. doi: 10.1109/TPAMI.2012.47.

Abstract

VCells, the proposed Edge-Weighted Centroidal Voronoi Tessellations (EWCVTs)-based algorithm, is used to generate superpixels, i.e., an oversegmentation of an image. For a wide range of images, the new algorithm is capable of generating roughly uniform subregions and nicely preserving local image boundaries. The undersegmentation error is effectively limited in a controllable manner. Moreover, VCells is very efficient with core computational cost at O(K√n(c)·N) in which K, n(c), and N are the number of iterations, superpixels, and pixels, respectively. Extensive qualitative discussions are provided, together with the high-quality segmentation results of VCells on a wide range of complex images. The simplicity and efficiency of our model are demonstrated by complexity analysis, time, and accuracy evaluations.

摘要

VCells,所提出的基于边缘加权质心 Voronoi 三角剖分(EWCVTs)的算法,用于生成超像素,即图像的过分割。对于广泛的图像,新算法能够生成大致均匀的子区域,并很好地保留局部图像边界。细分错误可以有效地以可控的方式限制。此外,VCells 非常高效,核心计算成本为 O(K√n(c)·N),其中 K、n(c)和 N 分别是迭代次数、超像素和像素的数量。提供了广泛的定性讨论,并展示了 VCells 在广泛的复杂图像上的高质量分割结果。通过复杂度分析、时间和准确性评估,证明了我们模型的简单性和效率。

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