Ben Ayed Ismail, Hennane Nacera, Mitiche Amar
Institut National de la Recherche Scientifique, INRS-EMT, Montréal, QC H5A 1K6, Canada.
IEEE Trans Image Process. 2006 Nov;15(11):3431-9. doi: 10.1109/tip.2006.881961.
Studies have shown that the Weibull distribution can model accurately a wide variety of images. Its parameters index a family of distributions which includes the exponential and approximations of the Gaussian and the Raleigh models widely used in image segmentation. This study investigates the Weibull distribution in unsupervised image segmentation and classification by a variational method. The data term of the segmentation functional measures the conformity of the image intensity in each region to a Weibull distribution whose parameters are determined jointly with the segmentation. Minimization of the functional is implemented by active curves via level sets and consists of iterations of two consecutive steps: curve evolution via Euler-Lagrange descent equations and evaluation of the Weibull distribution parameters. Experiments with synthetic and real images are described which verify the validity of method and its implementation.
研究表明，威布尔分布能够准确地对多种图像进行建模。其参数索引了一族分布，其中包括指数分布以及在图像分割中广泛使用的高斯模型和瑞利模型的近似。本研究通过变分方法研究了威布尔分布在无监督图像分割和分类中的应用。分割泛函的数据项衡量每个区域中图像强度与威布尔分布的一致性，该分布的参数与分割一起确定。泛函的最小化通过水平集的活动曲线来实现，包括两个连续步骤的迭代：通过欧拉 - 拉格朗日下降方程进行曲线演化以及威布尔分布参数的评估。描述了对合成图像和真实图像的实验，验证了该方法及其实现的有效性。
