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首页> 外文期刊>Computer methods in biomechanics and bio >Image segmentation and adaptive superpixel generation based on harmonic edge-weighted centroidal Voronoi tessellation
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Image segmentation and adaptive superpixel generation based on harmonic edge-weighted centroidal Voronoi tessellation

机译：基于谐波边缘加权质心Voronoi细分的图像分割和自适应超像素生成

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摘要

In this paper, we extend the basic edge-weighted centroidal Voronoi tessellation (EWCVT) for image segmentation to a new advanced model, namely harmonic edge-weighted centroidal Voronoi tessellation (HEWCVT). This extended model introduces a harmonic form of clustering energy by combining the image intensity with cluster boundary information. Improving upon the classic centroidal Voronoi tessellation (CVT) and EWCVT methods, the HEWCVT algorithm can not only overcome the sensitivity to the seed point initialisation and noise, but also improve the accuracy and stability of clustering results, as verified in several types of images. We then present an adaptive superpixel generation algorithm based on HEWCVT. First, an innovative initial seed sampling method based on quadtree decomposition is introduced, and the image is divided into small adaptive segments according to a density function. Then, the local HEWCVT algorithm is applied to generate adaptive superpixels. The presented algorithm is capable of generating adaptive superpixels while preserving local image features efficiently.
机译：在本文中，我们将用于图像分割的基本边缘加权质心Voronoi曲面细分（EWCVT）扩展到新的高级模型，即谐波边缘加权质心Voronoi曲面细分（HEWCVT）。该扩展模型通过将图像强度与聚类边界信息相结合，引入了聚类能量的谐波形式。 HEWCVT算法改进了经典的质心Voronoi曲面细分（CVT）和EWCVT方法，不仅克服了对种子点初始化和噪声的敏感性，而且还提高了聚类结果的准确性和稳定性，这已在多种类型的图像中得到了验证。然后，我们提出了一种基于HEWCVT的自适应超像素生成算法。首先，介绍了一种创新的基于四叉树分解的初始种子采样方法，然后根据密度函数将图像分为小的自适应段。然后，将局部HEWCVT算法应用于生成自适应超像素。所提出的算法能够生成自适应超像素，同时有效地保留局部图像特征。

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