A recursive color image edge detection method using Green's function approach

In this paper, an extended version of image edge detector using Green's function approach is proposed for detection of edges in the color vector space field. In the proposed method, the relationship between the Red, Green and Blue components is considered to design a differential operator for detection of edges in color images. By using the proposed operator, partial derivatives of all components of color image can simultaneously affect on the edge detection process. Therefore the proposed method can preserve the vector nature of color images during the edge processing stages. Also, the proposed method is compared both quantitatively and qualitatively with other color edge detectors. Experimental results show that the proposed method can efficiently preserve the edges even when the color images corrupted with different levels of noise.     

光突发交换边缘路由器性能分析

本文详细分析了各种因素对光突发交换边缘路由器的性能的影响.结果显示,不同服务等级的时延要求可以通过控制组装的最大时延得以保证;不同等级数据包长的大范围变动会导致信道分配的不公平性;边缘节点越多,丢包率越高;在总输出容量一定的情况下,增加输出数据信道数可以在获得低的丢包率的同时达到较高的通道利用率,但随着边缘节点数的增加,增加输出信道数对丢包率的改善作用下降.     

光锥与CCD耦合器件调制传递函数的测试方法

采用直接对倾斜刀口成像的方法测量光锥与CCD耦合器件的调制传递函数.这种方法对静止刀口成像,利用刀口与光锥纤维列间的一定倾斜角度,在垂直于刀口的不同行之间形成不同的值,代替了传统刀口扫描中的扫描装置,并无需保证刀口与阵列的平行,相应地减小了测量误差. 在数据处理中采用了二次平均法,即对同一幅图像得出的不同位置值进行平均及多次测量结果平均,在一定程度上消除了由于离散性产生的空间非平移不变性的影响.     

二维图象中二阶导数滤波器边缘定位误差的理论分析

本文从理论上讨论了低通滤波后二维图象中弯曲边缘的定位误差问题.边缘位置通常由二阶导数算子的零交叉点定义.研究表明:梯度方向上的二阶导数算子(secondderivativein gradient direction SDGD)产生向心的、可预测的边缘偏移;而线性拉普拉斯算子(Laplacian Operator)产生相反方向(离心)的可预测位置偏移.由此可推断:两者之和——称之为PLUS,将产生比其组成成份(SDGD 和 Laplace)更为精确的边缘定位算子.文章讨论了常用的低通滤波器(如 Gaussian 滤波器及 Tepee 滤波器)对边缘定位精度的影响.     

结合边缘信息和图像特征信息的曲波域遥感图像融合

曲波变换是一种更适合于图像处理的多尺度几何分析方法,具有比小波变换更强的方向选择和辨识能力,而且对图像边缘的表达更优于小波.结合色度-饱合度-亮度变换将其应用于合成孔径雷达图像和多光谱图像融合可以更好地表示图像中的有用特征.首先对多光谱图像进行色度-饱合度-亮度变换,得到亮度分量Ⅰ,对雷达图像和Ⅰ分量进行曲波变换得到粗尺度系数和细节尺度系数;将雷达图像的粗尺度系数和细节尺度系数进行叠加,计算归一化的曲波系数直方图,定义边缘有效因子,利用合成孔径雷达图像的特征信息将曲波变换系数分为均匀区、非均匀区和亮点目标区.然后采用相应的融合规则对融合图像的粗尺度系数进行处理,对细节尺度系数采用简单的直接取大方法,逆变换后得到新的亮度分量.用新的亮度分量替代原亮度分量进行逆色度-饱合度-亮度变换得到最终融合结果,利用统计类指标对融合结果进行评价.实验结果表明,该方法在保持光谱信息和提高空间分辨率上都有较好的效果.     

一种新的闪光照相图象边界检测方法

介绍了一种用于闪光X光照相图象的边界检测方法,称为计算对比法.对于投影成象,由于成象系统的非理想特性,实际照相特性的边界与理想成象图象的边界之间往往存在一位置差,我们称之为边界退化量.模糊图象的边界检测问题主要就是边界退化量的检测问题.本文提出的计算对比法根据闪光X光照相图象的退化模型进行计算模拟成象,用从模拟图象上得出的边界退化量对实际成象图象的位置进行校正.从而达到精确检测实际图象的目的.用这种方法对多幅模拟成象图象进行了处理.     

一种新的珍珠层厚度测量方法

珍珠层厚度是是珍珠价值最主要的衡量指标之一,目前珍珠层厚度的无损测量方法主要采用光学相干层析成像技术和X射线技术。光学相干层析成像技术在实际测量时精度较低。在研究X射线测量原理的基础上,提出了一种新的珠层厚度测量方法。首先介绍了灰度图像的边缘识别算法和珍珠核、珍珠外圆度拟合算法,其次提出了一种引入对照机制的珍珠层测量算法,最后还对算法的误差进行了分析,算法的误差可以控制在0.02%以内。实际的测量也验证了本测量方法具有很高的精度。     

On the localization of the antisymmetric flexural edge waves for obtuse angles

It is well known that the existence of edge waves is directly related to the localization of the acoustic field in the wedge. In this paper, it is shown experimentally and numerically that, for wedge angles smaller than about 100° (this angle may vary from one material to an other), the edge modes are confined in the tip of the wedge and may be considered as localized. For higher wedge angles, the analysis of the results shows a delocalization of the guided waves, which induces a new repartition of the acoustical energy in the wedge and a decrease in the amplitude of the wedge wave. This observation is numerically verified via an analysis in the time domain. Experiments realized on obtuse wedges demonstrate that the first ASF mode may be detected for wedge angles up to about 110°.     

复杂背景下扩展目标极形态分形分割算法

 用极形态算子优化分形模型,提出了一种复杂背景下的扩展目标分割新算法。首先分析极坐标系中可变尺度形态结构算子的旋转缩放特性,并以此为基础进行极形态运算,再利用最小二乘法提取分形尺度误差进行二值化,然后进行边界跟踪,依据先验知识抑制背景团块,最后保留并填充面积最大的目标团块。仿真实验证明能够有效分割复杂背景下的扩展目标,并较好地保留了目标形状特征。     

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ?₂ data fidelity term and ?₁ sparsity regularization term. Rather than manually setting the regularization parameter for the ?₁ term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression.