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Multiphase segmentation based on new signed pressure force functions and one level set function

机译:基于新的有符号压力函数和一级设置函数的多相分割

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In this paper we propose a new model to detect multiple objects of various intensities in images having maximum, minimum, or middle-intensity background by evolving only one level set function. In this model, a new signed pressure force function based on novel generalized averages is used for segmentation of images with maximum or minimum intensity background. For images with middle-intensity backgrounds, which are indeed challenging for 2-phase models, we propose a new product generalized signed pressure force function. Finally, to give experimental and qualitative evidence, our model is tested on both synthetic and real images with the Jaccard similarity index. The experimental and qualitative results reveal that the proposed method is efficient in both global and selective segmentation. Our new model is also tested on color images and the results are compared with the state-of-the-art models.
机译:在本文中,我们提出了一种新模型,该模型可以通过仅演化一个级别集函数来检测具有最大,最小或中等强度背景的图像中多个强度不同的对象。在此模型中,基于新的广义平均值的新的带符号压力函数用于分割具有最大或最小强度背景的图像。对于具有中等强度背景的图像(对于两相模型而言确实具有挑战性),我们提出了一种新产品广义符号压力函数。最后,为了提供实验和定性证据,我们使用Jaccard相似性指数对合成和真实图像进行了测试。实验和定性结果表明,该方法在全局和选择性分割方面均有效。我们的新模型还在彩色图像上进行了测试,并将结果与​​最新模型进行了比较。

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