基于图像对数极坐标变换的多分辨率相关匹配算法

常规的图像相关匹配算法通常只具有平移不变性，而不具有尺度和旋转不变性，这就给存在尺度和旋转变化图像间的匹配问题设置了障碍。针对这一问题，提出了一种基于图像对数极坐标变换的相关匹配算法，并融入了高斯金字塔多分辨率机制以提高算法的效率。由于对数极坐标变换具有尺度和旋转不变性，因此该方法能够较好地克服传统图像匹配算法的缺陷，不仅可以完成存在更一般运动的图像间的匹配，而且在匹配的同时还可以对图像的尺度和旋转变化量进行估计。     

传递对准中主惯导速度匹配量的实时构造算法

研究了当机载主惯导和弹载子惯导分别采用地理坐标系和准惯性坐标系作为导航坐标系时，传递对准过程中主惯导速度匹配量的实时构造算法。提出了利用主惯导导航参数实时构造主惯导速度匹配量的地速转换和修正算法，利用主惯导加速度计输出实时构造主惯导速度匹配量的比力积分算法，以及利用主惯导导航参数和加速度计输出实时构造主惯导速度匹配量的比力积分修正算法。仿真结果表明，3种实时算法在算法精度和计算量方面各有所长。     

基于Kriging插值的数字地图生成算法研究

提出基于Kriging法进行水深数据插值形成规则网格数字高程模型的算法,对海底数字地图的模拟具有重要参考价值,是海底地形匹配定位技术研究的关键.数字仿真结果证明该算法可行.     

Agent的辅助服务体系及其中介Agent的设计

文章分析了原Agent平台ABFSC在开放的Internet环境中的明显不足:熟人必须静态绑定;Agent能力必须事先知道。提出了基于中介Agent的辅助服务体系,它实现了Agent能力的动态发布和Agent熟人的动态绑定。并重点讨论了中介Agent的设计与实现中的关键技术。     

一种基于结构匹配的指纹匹配算法

提出了一种基于结构匹配的指纹匹配算法,不仅考虑了指纹的全局特性而且利用了指纹局部结构的平移和旋转不变性。该算法可克服在采集指纹图像时由于非线性形变所引起的细节点的位置、细节点的方向及指纹图像中细节点之间距离改变产生的影响,降低了匹配算法的拒识率。试验结果表明所提出的方法确实提高了指纹匹配算法的有效性。     

An output-based adaptive iterative learning controller for high relative degree uncertain linear systems

In this paper, we derive an output tracking error model based on signals filtered from plant input and output, and then present a new output-based adaptive iterative learning controller for repeatable linear systems with unknown parameters, high relative degree, initial resetting error, input disturbance and output noise. The proposed controller solves the important robustness issues without assuming the bounds of uncertainties to be sufficiently small and can be applied to high relative degree plants without using output differentiation. Control parameters are updated between successive iterations so as to compensate for unknown system parameters and uncertainties. It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters. Furthermore, the learning speed is easily improved if the learning gain is increased.     

Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.     

Iterative learning control design based on composite energy function with input saturation

In this work, an iterative learning control scheme is designed for a class of nonlinear uncertain systems with input saturation. The analysis of convergence in the iteration domain is based on composite energy function, which consists of both input and state information along the time and iteration axes. Through rigorous analysis, the learning convergence in the iteration domain can be guaranteed under the input saturation, provided the desired trajectory is realizable within the saturation bound.     

Scaled and permuted string matching

The goal of scaled permuted string matching is to find all occurrences of a pattern in a text, in all possible scales and permutations. Given a text of length n and a pattern of length m we present an O(n) algorithm.     

Recognition of shapes by attributed skeletal graphs

In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions.