Implicit Active Contours Driven by Local Binary Fitting Energy
Implicit Active Contours Driven by Local Binary Fitting Energy
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DOI：
10.1109/cvpr.2007.383014
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
Chunming Li;C. Kao;J. Gore;Z. Ding
中科院分区：
文献类型：
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作者：
Chunming Li;C. Kao;J. Gore;Z. Ding
局部图像信息对于灰度不均匀图像的精确分割至关重要。然而，在流行的基于区域的活动轮廓模型，如分段常数模型，没有嵌入图像的局部区域的信息。在本文中，我们提出了一个基于区域的活动轮廓模型，能够利用图像信息的局部区域。本文的主要贡献是引入了一个具有核函数的局部二值拟合能量，从而能够提取准确的局部图像信息。因此，我们的模型可以用来分割图像的强度不均匀性，这克服了分段常数模型的局限性。与其他主要的基于区域的模型，如分段光滑模型的比较，显示了我们的方法在计算效率和精度方面的优势。此外，该方法在图像去噪方面也有很好的应用前景。
Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piece-wise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising.