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Image segmentation based on novel adaptive bidirectional balloon force model

机译:基于新型自适应双向气球力模型的图像分割

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摘要

A novel adaptive bidirectional balloon force model for image segmentation is proposed in this paper. Active contour model based on a balloon force field has been widely used to detect object boundaries. However, the unidirectional movement property demands that the active contour should be completely placed inside or outside the boundary, and this can easily cause leaking through weak edges and seriously limit the application of the balloon force model. Therefore, the adaptive bidirectional balloon force model based on magnetostatic field is studied as a new force model. Experimental results show that, compared with gradient vector flow model (GVF) and vector field convolution model (VFC), the proposed method significantly improves the active contour in capturing complex geometries and dealing with difficult initializations.
机译:提出了一种新型的自适应双向气球力图像分割模型。基于气球力场的主动轮廓模型已被广泛用于检测物体边界。但是,单向运动特性要求活动轮廓必须完全放置在边界内或边界外,这很容易导致通过弱边缘泄漏,并严重限制了气球力模型的应用。因此,研究了基于静磁场的双向双向气球力模型。实验结果表明,与梯度矢量流模型(GVF)和矢量场卷积模型(VFC)相比,该方法在捕获复杂几何形状和处理困难的初始化方面显着提高了活动轮廓。

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