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Color image segmentation by combining the convex active contour and the Chan Vese model

机译：通过结合凸主动轮廓线和Chan Vese模型进行彩色图像分割

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摘要

In this paper, we present a robust and computationally efficient image segmentation technique based on a hybrid convex active contour and the Chan-Vese (CV) model. The proposed algorithm overcomes the drawbacks of existing image segmentation techniques which are heavily dependent upon the initial user input. Here, we propose to combine region-based and boundary-based techniques for segmentation so that we guarantee robustness across all types of images. We start with a either a geodesic-based or a dynamic region merging (DRM)-based contour before using the CV model. Contrary to the basic geodesic model, the random walk technique, and the snake-based convex active contour model, our algorithm works with minimal input and is shown to be independent of the location of the input pixels provided by the user. The algorithm works by initiating a contour which is either based on the geodesic distance or the DRM model. This contour is then used with the CV model to further refine the segmentation results. We tested the proposed algorithm on several standard databases using both subjective and objective measures. Our experimental results show that the proposed algorithm outperforms recently proposed approaches over indoor and outdoor images in terms of both processing time and segmentation accuracy.
机译：在本文中，我们提出了一种基于混合凸主动轮廓线和Chan-Vese（CV）模型的鲁棒且计算效率高的图像分割技术。所提出的算法克服了现有图像分割技术的缺点，该技术在很大程度上取决于初始用户输入。在这里，我们建议结合基于区域和基于边界的技术进行分割，以便保证所有类型图像的鲁棒性。在使用CV模型之前，我们先从基于测地线或基于动态区域合并（DRM）的轮廓开始。与基本的测地线模型，随机游走技术和基于蛇的凸主动轮廓模型相反，我们的算法使用最少的输入即可工作，并且显示出与用户提供的输入像素的位置无关。该算法通过启动基于测地距离或DRM模型的轮廓来工作。然后将该轮廓与CV模型一起使用，以进一步优化分割结果。我们使用主观和客观方法在几个标准数据库上测试了该算法。我们的实验结果表明，该算法在处理时间和分割精度上都优于最近提出的室内和室外图像处理方法。

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