论文标题
图像分割的最先进的模糊主动轮廓模型
State-of-The-Art Fuzzy Active Contour Models for Image Segmentation
论文作者
论文摘要
图像分割是每个图像分析任务的初始步骤。几十年来，在文献中提出了各种各样的分割算法，并获得了一定的成功。其中，基于模糊的活性轮廓模型在过去的十年中引起了研究人员的关注，这导致了各种方法的发展。良好的分割算法应在包含噪声，模糊，低对比度，区域内均匀性等的大量图像中表现良好。但是，通常在有限的图像数量上评估了通常在有限的图像上评估了大多数现有基于模糊的活性轮廓模型的性能。在本文中，我们的目的是从理论的角度回顾现有的模糊主动轮廓模型，并在各种条件下的大量图像上对它们进行实验评估。在各种图像下的分析提供了对各种模糊主动轮廓模型的优势和劣势的客观见解。最后，我们讨论了有关此特定主题的几个问题和未来的研究方向。
Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region in-homogeneity, etc. However, the performances of the most of the existing fuzzy energy based active contour models have been evaluated typically on the limited number of images. In this article, our aim is to review the existing fuzzy active contour models from the theoretical point of view and also evaluate them experimentally on a large set of images under the various conditions. The analysis under a large variety of images provides objective insight into the strengths and weaknesses of various fuzzy active contour models. Finally, we discuss several issues and future research direction on this particular topic.
