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Image Segmentation using GAC Model Combining with GVF and Balloon Force

机译:GAC模型结合GVF和球囊力的图像分割

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摘要

The geodesic active contour (GAC) model has been widely used due to the high precision of edge detection and the continuity of boundaries. However, compared to the gradient vector flow (GVF) field, it usually has poor performance in the object boundaries with concave shape. In this paper, the improvements that integrate the good performance of the GAC model and the GVF field are described in detail and the GAC_GVF&B model combining the GAC model with the GVF field and a balloon force is proposed. The experimental results demonstrate that the proposed method is insensitive to the positions of initial curves and can segment complex-shape or multi-objects images correctly with higher convergence.
机译:测地线活动轮廓(GAC)模型由于边缘检测的高精度和边界的连续性而被广泛使用。但是,与梯度矢量流(GVF)场相比,它在凹形物体边界中的性能通常较差。本文详细描述了将GAC模型和GVF场的性能集成在一起的改进措施,并提出了将GAC模型与GVF场和球囊力相结合的GAC_GVF&B模型。实验结果表明,该方法对初始曲线的位置不敏感,可以较好地收敛复杂形状或多目标图像。

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