Active contour model based on variable exponent p-Laplace equation for image segmentation

In this paper, we propose a region-based active contour model for image segmentation. By combining the region fitting energy based on coefficient of variation with the variable exponent p-Laplace energy, the proposed method can perform well in segmenting complex images. The region fitting energy conducts the evolving curve to reach the boundaries of the objects, and the p-Laplace energy can handle the topological changes and extract the boundaries accurately. In order to eliminate the re-initialization step, an augmented Lagrangian method is employed to solve the optimization problem. The results of experiments on synthetic and real images demonstrate that our method can successfully segment complex object boundaries, and it is robust to noise and not sensitive to the initial position of contours.     

Bézier-Said型曲线

为了扩大自由型曲线曲面的选择范围,提出了一族介于Bézier曲线与Said-Ball曲线之间的新型曲线,在形式上将Bézier曲线与Said-Ball曲线统一起来,并对这一族曲线的性质进行了研究。同时给出了有关的升阶公式以及将基函数用Bernstein多项式来表示的系数公式。     

Entropy-Based Fuzzy Least Squares Twin Support Vector Machine for Pattern Classification

Neural Processing Letters - Least squares twin support vector machine (LSTSVM) is a new machine learning method, as opposed to solving two quadratic programming problems in twin support vector...     

三角域上三阶T-Bezier曲面片的构造与设计

在计算机辅助几何设计中,T-Bezier曲线曲面被视为一种新的自由曲线曲面造型工具得到广泛研究,然而其曲面都是张量积形式的,为了进一步研究非多项式空间中的T-Bezier基,完善其关于三角域部分的理论,构造了满足正性、权性、对称性、边界性质和线性无关性的基函数,并证明了三角域上相应曲面的一些性质;最后给出了一些应用。     

基于特征值分解的中心支持向量机算法

针对广义特征值中心支持向量机(GEPSVM)训练和决策过程不一致问题,该文提出一类改进的基于特征值分解的中心支持向量机,简称为IGEPSVM。首先针对二分类问题提出了基于特征值分解的中心支持向量机,然后基于一类对余类策略将其推广到多类分类问题。将GEPSVM求解广义特征值问题转化为求解标准特征值问题,降低了计算复杂度。引入了一个新的参数,可以调节模型的性能,提高了GEPSVM的分类精度。提出了基于IGEPSVM的多类分类算法。实验结果表明,与GEPSVM算法相比较,IGEPSVM不仅提高了分类精度,而且缩短了训练时间。     

拟Timmer曲线

三次Timmer曲线能够插值的控制多边形中间控制边的中点,但它却不能保证与该控制边相切,且三次Timmer曲线不具有凸包性。针对于此,提出了一类曲线（称之拟Timmer曲线）,拟Timmer曲线不仅具有与Timmer曲线所类似的性质,而且具有凸包性,且能够插值中间控制边的中点,并与之相切。     

一类T-Bézier三角曲面渐渐迭代算法

渐进迭代逼近在散乱点数据的拟合及逆向工程中有重要应用,研究了一类T-Bézier三角曲面的渐进迭代算法;提出了T-Bézier三角曲面渐进迭代算法,并分析了算法的收敛性;基于2-范数给出了渐进迭代算法的逼近误差。最后,举例说明了该算法的有效性及应用。     

KPCA method based on within-class auxiliary training samples and its application to pattern classification

Principal component analysis (PCA) and kernel principal component analysis (KPCA) are classical feature extraction methods. However, PCA and KPCA are unsupervised learning methods which always maximize the overall variance and ignore the information of within-class and between-class. In this paper, we propose a simple yet effective strategy to improve the performance of PCA and then this strategy is generalized to KPCA. The proposed methods utilize within-class auxiliary training samples, which are constructed through linear interpolation method. These within-class auxiliary training samples are used to train and get the principal components. In contrast with conventional PCA and KPCA, our proposed methods have more discriminant information. Several experiments are respectively conducted on XM2VTS face database, United States Postal Service (USPS) handwritten digits database and three UCI repository of machine learning databases, experimental results illustrate the effectiveness of the proposed method.