Institute for Pattern Recognition and Artificial Intelligence and State Key Laboratory for Multi-spectral Information Processing Technologies, Huazhong University of Science and Technology, Wuhan, China.
IEEE Trans Image Process. 2012 Jan;21(1):284-96. doi: 10.1109/TIP.2011.2160955. Epub 2011 Jun 30.
In this paper, an iterative narrow-band-based graph cuts (INBBGC) method is proposed to optimize the geodesic active contours with region forces (GACWRF) model for interactive object segmentation. Based on cut metric on graphs proposed by Boykov and Kolmogorov, an NBBGC method is devised to compute the local minimization of GAC. An extension to an iterative manner, namely, INBBGC, is developed for less sensitivity to the initial curve. The INBBGC method is similar to graph-cuts-based active contour (GCBAC) presented by Xu , and their differences have been analyzed and discussed. We then integrate the region force into GAC. An improved INBBGC (IINBBGC) method is proposed to optimize the GACWRF model, thus can effectively deal with the concave region and complicated real-world images segmentation. Two region force models such as mean and probability models are studied. Therefore, the GCBAC method can be regarded as the special case of our proposed IINBBGC method without region force. Our proposed algorithm has been also analyzed to be similar to the Grabcut method when the Gaussian mixture model region force is adopted, and the band region is extended to the whole image. Thus, our proposed IINBBGC method can be regarded as narrow-band-based Grabcut method or GCBAC with region force method. We apply our proposed IINBBGC algorithm on synthetic and real-world images to emphasize its performance, compared with other segmentation methods, such as GCBAC and Grabcut methods.
本文提出了一种基于迭代窄带的图割（INBBGC）方法，用于优化具有区域力的测地线主动轮廓（GACWRF）模型，以进行交互式目标分割。基于 Boykov 和 Kolmogorov 提出的图割度量，设计了一种 NBBGC 方法来计算 GAC 的局部最小化。为了减少对初始曲线的敏感性，提出了一种迭代方式的扩展，即 INBBGC。INBBGC 方法类似于 Xu 提出的基于图割的主动轮廓（GCBAC），并对它们的差异进行了分析和讨论。然后，我们将区域力集成到 GAC 中。提出了一种改进的 INBBGC（IINBBGC）方法来优化 GACWRF 模型，从而可以有效地处理凹区域和复杂的真实世界图像分割。研究了两种区域力模型，如均值模型和概率模型。因此，GCBAC 方法可以被视为我们提出的 IINBBGC 方法在没有区域力的特殊情况。当采用高斯混合模型区域力时，我们提出的算法也可以被分析为类似于 Grabcut 方法，并且带宽区域扩展到整个图像。因此，我们提出的 IINBBGC 方法可以被视为基于窄带的 Grabcut 方法或具有区域力的 GCBAC 方法。我们将我们提出的 IINBBGC 算法应用于合成和真实世界图像，以强调其性能，与其他分割方法（如 GCBAC 和 Grabcut 方法）进行比较。
