廊固凹陷大兴断层对油气分布的控制研究

廊固凹陷断裂系统复杂，其中大兴断层是中西部的控盆边界断层，它通过对凹陷的构造样式、沉积特征的控制进而影响整个凹陷的油气分布。从区域构造背景和盆地的沉积演化角度，按不同段落断裂展布的方向、产状的陡缓、对沉积的控制作用以及不同层段物质组份的差异，将大兴断层划分为4段：北段、中北段、中南段和南段，各段又可进一步分为上下不同的两段。各段断层的结构、形态、伴生构造、活动时间等都存在较明显差异，通过控制物源而控制沉积相的展布、储层物性和生储盖组合。相应的伴生构造和沉积物发育特征导致构造、构造-岩性组合控制了油气的分布：北段下段、中北段下段近湖一侧是廊固凹陷中西部有利的油气聚集带；中北段上段发育的生物气藏具有一定的勘探前景。     

On Burst Length Distribution and Preemption Probabilities in OBS Networks with Burst Segmentation

Recently, preemption techniques have attracted considerable attention as a means to provide differentiated quality of service in optical burst switching (OBS) networks. This paper is focused on the analysis of preemption probabilities for bursts within the same priority class. As proposed by Vokkarane and Jue ((2003)* IEEE J Select Areas Commun 21(7): 1198–1209) an incoming burst will preempt the burst in service, within the same priority class, if the residual length of the burst in service is smaller than the incoming burst length. For a general case with wavelength conversion, the preemption probability of contending bursts with a generic service distribution, not necessarily exponential, is analyzed. First, we show that the size distribution for the preempting bursts is shifted to larger values, in comparison to the original burst size distribution. Second, we obtain an upper and lower bound for the preemption probability. Finally, the asymptotic behavior of the OBS switch is analyzed showing that preemption will always occur for a very large number of wavelengths.     

一种新颖的基于非压缩数字视频的水印盲检测算法

数字水印是一种嵌入到多媒体数据中用来进行版权标识的工具。在众多的宿主媒体中，数字视频因具有隐藏容量大、透明性好、鲁棒性强等诸多优点而受到日益广泛的关注。但是很多文献中提到的视频水印都是从数据流中提取单帧图像进行处理，这类算法与静态图像的水印方法如出一辙，没有充分利用视频文件的各种特性。而且对帧平均、视频压缩等常见的运动图像攻击方法十分敏感。针对这些问题，本文以非压缩视频文件为实验对象，结合人类视觉模型和彩色图像场景分割的方法，提出并实现了一种基于视频时间轴的数字水印盲检测算法。实验结果表明算法有效实用。     

区域分割法辅助计算复杂图像面积

介绍计算具有复杂边缘轮廓的图像面积的区域分割法。采用鼠标多边形作图法近似圈定待处理的图像区域。本文由格林定理出发得到有限连续边界与其包围区域面积之间的函数关系,并且应用这一函数关系得出多边形与其包围区域面积的数学关系,近似得到图像区域面积。文中以烟幕图像为例对上述结论进行了验证。文章最后给出了在VC++环境下实现多边形分割的部分源代码。     

A VLSI architecture for a half-edge-based corner detector

Corner detection is a low-level feature detection operator that is of great use in image processing applications, for example, optical flow and structure from motion by image correspondence. The detection of corners is a computationally intensive operation. Past implementations of corner detection techniques have been restricted to software. In this paper we propose an efficient very large-scale integration (VLSI) architecture for detection of corners in images. The corner detection technique is based on the half-edge concept and the first directional derivative of Gaussian. Apart from the location of the corner points, the algorithm also computes the corner orientation and the corner angle and outputs the edge map of the image. The symmetrical properties of the masks are utilized to reduce the number of convolutions effectively, from eight to two. Therefore, the number of multiplications required per pixel is reduced from 1800 to 392. Thus, the proposed architecture yields a speed-up factor of 4.6 over conventional convolution architectures. The architecture uses the principles of pipelining and parallelism and can be implemented in VLSI.     

高分辨率合成孔径雷达图像舰船检测方法

寻找针对高分辨率SAR图像的舰船目标检测算法。利用KSW双阈值分割技术，其效果比传统检测方法好，有利于进一步的目标分类和识别。且必须根据SAR图像分辨率来选择舰船检测算法。     

Quadtree-based genetic algorithm and its applications to computer vision

Many computer vision problems can be formulated as optimization problems. Presented in this paper is a new framework based on the quadtree-based genetic algorithm that can be applied to solve many of these problems. The proposed algorithm incorporates the quadtree structure into the conventional genetic algorithm. The solutions of image-related problems are encoded through encoding the corresponding quadtrees, and therefore, the 2D locality within a solution can be preserved. Examples addressed using the proposed framework include image segmentation, stereo vision, and motion estimation. In all cases, encouraging results are obtained.     

EM procedures using mean field-like approximations for Markov model-based image segmentation

Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments.     

Cluster analysis in industrial market segmentation through artificial neural network

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.     

SAR图像组合分割算法

由于存在乘性斑点噪声的影响,给SAR图像分割造成很大困难,本文研究利用最大似然区域增长分割算法,提出区域增长前引入自适应边缘检测,图像分割性能获得较大提高.介绍了我们研究的分割性能优异的模拟退火分割算法,提出了分割性能接近模拟退火并且在SAR图像分割中比较实用的模拟退火和最大似然混合分割算法,并给出了这些分割算法的分割性能比较,以及在实际分割SAR图像时应当如何选择分割算法.