现有的距离图像对准算法，一般是采用最近点之间的距离和作为评价函数来估计运动参数。这种基于点对间距离的评价函数的缺点是，不同距离图像之间的最近点不一定是物体表面同一点的采样，而且不同采样点所代表的物体表面面积也不同。这些问题使得基于点对间距离度量的对准算法存在不精确对应的问题，因而对准精度不高，对初始运动参数敏感。提出一种基于表面间距离度量的对准算法。通过构造三角网格来近似表示物体的表面，采用三角网格间最近距离的均值作为评价函数来估计运动参数。推导并简化了表面间距离计算的积分公式。实验结果表明，该算法有较高的对准精度，而且收敛速度较快，抗噪声能力较强。

Selecting an optimal subset from original large feature set in the design of pattern classiffer is an important and difficult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential forward (backward) foating search methods. Based on the results of experiments, tabu search is shown to be a promising tool for feature selection in respect of the quality of obtained feature subset and computation effciency. The e!ects of parameters in tabu search are also analyzed by experiments.

This paper presents three heuristic algorithms for optimal polygonal approximation of digital planar curves. We use genetic algorithm (GA), improved genetic algorithm based on Pareto optimal solution (GAP) and tabu search (TS) to obtain a near optimal polygonal approximation. Compared to the famous Teh-Chin algorithm, our algorithms have obtained the approximated polygons with less number of vertices and less approximation error. Compared to the dynamic programming algorithm, the processing time of our algorithms are much less.

图像水印容量分析是研究在图像中究竟可以隐藏多少信息的问题，研究如何在满足水印不可感知性和鲁棒性的前提下传递尽可能多的水印信息。为了研究水印容量，一般把水印的嵌入和提取视为一次通信过程。该文认为应该在满足水印不可感知性的前提下，基于图像的内容自适应地限制水印的嵌入功率。该文分析了现有水印容量分析方法的不足，提出了一种内容自适应的水印嵌入算法，在此基础上，提出了一种利用噪声可见性函数对水印嵌入功率进行自适应限定的水印容量分析方法，并讨论了盲检测和非盲检测情况下的水印容量问题。

This paper presents a blind watermarking algorithm based on the Hopfield neural network. The watermarking capacityof this algorithm is discussed. According to the research, the attraction basin of neural network determines the atermarking capacity.

In watermarking schemes, watermarking can be viewed as a form of communication problems. Almost all of previous works on image watermarking capacity are based on information theory: using Shannon formula to calculate the capacity of watermarking. In this paper, we present a blind watermarking algorithm using Hopfield neural network, and analyze watermarking capacity based on neural network. In our watermarking algorithm, watermarking capacity is decided by attraction basin of associative memory.