Pixel-clarity-based multifocus image fusion

Due to the limited depth-of-field of optical lenses, it is difficult to get an image with all objects in focus. One way to overcome this problem is to take several images with different focus points and combine them into a single composite which contains all the regions full focused. This paper describes a pixel-clarity-based multifocus image fusion algorithm. The characteristic of this approach is that the pixels of the fused image are selected from the clearest pixels in the input images according to pixel clarity criteria. For each pixel in the source images, the pixel clarity is calculated. The fusion procedure is performed by a selection mode according to the magnitude of pixel clarity. Consistency verification is performed on the selected pixels. Experiments show that the proposed algorithm works well in multifocus image fusion.     

A united optimum images fusion based on analysis of color distortion

In remote sensing community, IHS (intensity, hue, and saturation) transform is one of the most commonly used fusion algorithm. A study on IHS fusion indicates that the color distortion cannot be avoided.Meanwhile, wavelet decomposition has a property of frequency division in transform domain. And the statistical property of wavelet coefficient reflects those significant features. So, a united optimal fusion method, which using the statistical property of wavelet decomposition and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component I is fused on feature level with multi-resolution wavelet in IHS space, and the low frequency of intensity component I is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results show that it is a practical and effective method.     

抗漂移的状态反馈控制器设计及在数控系统中的应用

针对大部分抗漂移设计不能保证存在驱动饱和的开环稳定系统的闭环渐近稳定性的问题，在系统的量测噪声和外部扰动已经通过其他办法消除的条件下，设计了一种抗漂移的状态反馈控制器。该控制器将参考轨迹也加入到控制器设计中，极大地简化了控制器的参数设计并扩大了输入信号的范围。基于Lyapunov稳定性理论，证明了全系统的渐近稳定性及对参考输入的渐近跟踪性。将之用于数控系统控制器的设计中，仿真结果表明：无论饱和发生与否，该控制器不但可以保证系统的闭环稳定性，而且可以抑制 由于驱动饱和的存在而导致的跟踪性能的恶化，使得系统能够准确地跟踪参考输入。     

数控系统的自适应鲁棒控制器设计

基于李雅普诺夫稳定理论与动态逆方法，将自适应鲁棒控制与连续监督控制策略应用于数控系统的控制器设计中，用来补偿摩擦非线性、参数不确定性以及模型不确定性的影响，能显增强系统的稳定性和性能鲁棒性。对该方法进行了理论分析并与基于扰动观测器的控制器进行了对比仿真，仿真结果证明了理论分析的正确性。     

一种构建灰色预测模型的新算法

针对传统的GM（1，1）建模方法，对于历史数据采取等权处理的局限性，提出了GM（1，1）建模的新算法，不同的历史数据在预测值中具有不同的权重，并且解决了确定权值的学习算法。仿真结果表明了该算法的有效性。     

Feature-based fusion of infrared and visible dynamic images using target detection

We employ the target detection to improve the performance of the feature-based fusion of infrared and visible dynamic images, which forms a novel fusion scheme. First, the target detection is used to segment the source image sequences into target and background regions. Then, the dual-tree complex wavelet transform (DT-CWT) is proposed to decompose all the source image sequences. Different fusion rules are applied respectively in target and background regions to preserve the target information as much as possible. Real world infrared and visible image sequences are used to validate the performance of the proposed novel scheme. Compared with the previous fusion approaches of image sequences, the improvements of shift invariance, temporal stability and consistency, and computation cost are all ensured.     

Particle filter based visual tracking with multi-cue adaptive fusion

To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.     

Fusion of urban remote image based on multi-characteristics

A fusion approach is proposed to refine the resolution of urban multi-spectral images using the corresponding high-resolution panchromatic (PAN) images. Firstly, the two images are decomposed by wavelet transformation, and five texture features are extracted from high-frequency detailed sub-images. Then a multi-characteristics fusion rule is used to merge wavelet coefficients from the two images according to the extracted features. Experimental results indicate that, comparing with the non-characteristic methods, the proposed method can efficiently preserve the spectral information while improving the spatial resolution of the urban remote sensing images.     

基于多速率运动模型的多帧最近邻数据关联算法

为了提高标准最近邻数据关联算法的关联效果,提出了基于多速率运动模型的多帧最近邻数据关联算法．改正了Hong提出的多速率运动模型中关于过程噪声的一处错误,然后把该模型应用于最近邻数据关联算法,并推广到多帧情况．新算法使用序列关联量测更新目标状态,有效降低了最近邻算法中误关联对跟踪效果的影响．仿真结果表明,该算法在减少计算量的同时大大减少了跟踪丢失率。     

基于轨迹线性化控制方法的全驱动自主式水下航行器轨迹跟踪控制

基于轨迹线性化控制(Trajectory Linearization Control,TLC)方法研究了全驱动自主式水下航行器（AUV）的轨迹跟踪控制.利用微分方程奇异摄动理论中的时标分离思想,将全驱动AUV的轨迹跟踪控制系统分成快、慢回路,分别基于轨迹线性化思想设计出快、慢回路控制器;利用Lyapunov稳定性理论对闭环控制系统进行了稳定性分析,通过AUV爬升转弯过程的仿真验证了该控制方法的有效性.