将模糊集的隶属度函数矩阵嵌入到二维主成分分析以及二维线性判别分析中,形成了一种基于模糊2DPLA的新方法。该方法首先通过基于模糊的KNN方法求出隶属度函数矩阵;然后将隶属度函数矩阵从图像矩阵的水平方向和垂直方向分别嵌入到二维主成分分析和二维线性判别分析中,从而更好地实现降维;最后采用基于矩阵的F-范数代替传统的基于向量的2-范数进行分类度量。实验阶段,采用Yale Face Database B,ORL和FERET人脸数据库进行了测试和验证。结果证明,该方法具有较好的鲁棒性,并能获得较高的识别率。
In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.
