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A new particle swarm intelligence-based graph partitioning technique for image segmentation

机译:基于新的基于粒子群体的图像分割图形分区技术

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The advances in the image processing area demand for improvement in image segmentation methods. Effect of light and noise being ignored in image segmentation while tracing the objects of interest in addition to this texture is also one of the most important factors for analyzing an image automatically. Among the diverse segmentation methods, graph-based techniques are widespread because of their capabilities of generating accurate segmentation structures. In this paper, we have proposed a novel technique by using discrete particle swarm optimization and multilevel partitioning for segmentation of an image. The developed technique has lesser complexity, better efficiency and gives improved results than other methods.
机译:图像处理面积的提高对图像分割方法的改进需求的进步。 在图像分割中忽略了光和噪声的影响,同时追踪感兴趣的对象之外,除了这种纹理之外也是用于自动分析图像的最重要因素之一。 在不同的分割方法中,基于图形的技术是普遍的,因为它们的产生精确分割结构的能力。 在本文中,我们通过使用离散粒子群优化和多级分区来提出一种新颖的技术进行图像的分割。 开发技术具有较小的复杂性,更好的效率,并提供出的结果而不是其他方法。

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