振幅补偿匹配滤波中的旋转和尺度变化效应

本文研究了振幅补偿匹配滤波的旋转效应和尺度变化效应.计算机模拟的结果表明,这种滤波法对输入信号的旋转变化和尺度缩放变化,相对于传统的匹配滤波法及仅相位滤波法要敏感得多.文中还给出了这三和滤波下的相关峰值与旋转角度及尺度因子的关系曲线.     

基于谱线特征匹配的恒星光谱自动识别方法

我国正在实施的大型巡天项目(LAMOST项目),急需恒星光谱的自动识别系统。文章给出了一种基于谱线特征匹配的恒星光谱自动识别方法。该方法由以下主要步骤组成：(1) 利用小波变换的方法对观测光谱进行谱线特征提取;(2) 将提取出的特征和恒星谱线的特征模板进行相关匹配;(3) 根据相关匹配结果进行恒星光谱识别。通过对Sloan Digital Sky Survey (SDSS),Data Release Four (DR4)中的大量真实光谱数据实验表明,该方法具有对噪声鲁棒等特点,正确识别率高达96.7%。该方法可对相对定标的巡天光谱进行自动识别,符合LAMOST数据的要求,可为天文学家进行恒星和银河系的结构等研究提供帮助。     

二元计算全息干涉图匹配滤波器

本文通过分析比较,发现计算全息干涉图最适合作匹配滤波器,并实际制作了二元傅里叶变换计算全息干涉图,成功地进行了再现和光学相关实验。     

2f光电混合匹配滤波相关识别研究

提出一种光电混合匹配滤波相关识别方法,只用一个空间光调制器实现了匹配滤波相关识别。利用计算机完成匹配滤波器的设计以及匹配滤波器与输入目标频谱的卷积运算,根据最小欧氏投影编码原理将卷积结果转化为空间光调制器可以显示的灰度信号,再通过2f光学傅里叶变换系统,得到相关识别结果。实验结果表明,该方法可以得到明显的相关点,同时可以避免传统VLC中匹配滤波器的精确复位问题,而且该系统还可以应用于联合变换相关识别。     

1∶N识别光电混合匹配滤波相关器研究

利用空间匹配滤波的原理,提出了一种用于1∶N识别的光电混合匹配滤波相关器。利用计算机提前制作二值匹配滤波器,用计算机和FPGA直接计算识别所需要的功率谱,然后利用2f系统完成傅里叶变换,从而完成1∶N识别。从理论上对图像识别原理进行了分析,并用计算机仿真的方法加以验证,仿真结果表明,相关器输出锐的相关峰,具有很好的识别能力。     

基于灰度曲面匹配的虹膜识别方法

目前的虹膜识别都是采用在图像上提取特征点,并将特征点编码为固定长度的特征数据用于匹配的方式。这种方式使虹膜识别系统易受攻击。为了进一步提高虹膜识别系统的安全性和识别速度,提出了一种基于灰度曲面匹配的虹膜识别方法。该方法抛弃了特征提取和编码等传统操作,在特征分析的基础上直接利用了灰度曲面匹配的思想,首先对两幅图像中的像素灰度做差,得到灰度差曲面,然后求出该灰度差曲面的方差。将此方差作为衡量两个虹膜特征曲面之间距离的依据,并据此判定两个虹膜是否来自同一只眼睛。在给定阈值为40的前提下,正确识别率为96.89%,识别时间为53.2 ms。实验结果证明,该方法识别准确率高,识别速度快。     

一种改进的块匹配运动目标识别算法

鉴于传统的块匹配法在图像特征提取、匹配以及搜索等方面有诸多不足,复杂的边缘提取处理与基于灰度的匹配准则都使得系统的处理速度与精度降低,为了使识别处理更加快速精确,提出了一种改进的块匹配识别算法。通过拉普拉斯算子对图像特征进行增强,提高图像的特征信息;实验表明,针对320像素240像素大小的图像,改进算法的特征增强计算时间在Matlab中平均约为0005 4 s。     

利用合成谱在计算全息图中编码多个物体的方法

 提出在迂回位相傅里叶变换计算全息图中利用合成谱记录多个物体的方法。该方法利用多个物体合成的傅里叶频谱,代替传统的迂回位相型计算全息图中的单一频谱,在一幅计算全息图中完成多个物体的编码。再现时多个物体同时再现在同一衍射级的周围,并且再现像的形式可以多种多样。采用液晶空间光调制器(LC SLM)完成计算全息图的光学再现,实验验证了方法的有效性。结果表明：该方法可有效提高计算全息图的信息容量。     

基于数学形态学和匹配滤波的视网膜血管图像增强方法

针对视网膜图像成像过程的特点,提出了一种新的视网膜图像血管信息增强方法。由于存在光照不均匀的情况,采用同态滤波的方法设计了一种滤波器,对光照不均匀的情况进行了有效的校正;使用数学形态学中的Top Hat变换,对背景信息进行抑制,使得血管信息得到增强;最后进一步采用匹配滤波的方法,探测血管信息,使得血管信息的亮度得到提高,增强了与背景的对比度,血管信息具有明显的可视性。实验结果表明了提出的方法的有效性,对临床疾病的诊断及治疗具有较大的参考价值。     

介绍一种提高全息图衍射效率的显影方法

全息术中,研制高衍射效率的新型感光记录材料、探索提高衍射效率的显影工艺成为一个重要的研究课题。本文介绍我们的一项实验研究结果——一种简便易行的显影方法,它能明显地提高普通银盐干板全息图的衍射效率     

A novel method for recognizing face with partial occlusion via sparse representation

In this paper, we propose a novel method to recognize the partially occluded face images based on sparse representation. Generally, occlusions, such as glasses and scarf, fall on some random patch of image's pixels of test images, but which is known to be connected. In our method, all images are divided into several blocks and then an indicator based on linear regression technique is presented to determine whether a block is occluded. We utilize those uncontaminated blocks as the new feature of an image. Finally, the sparse representation-based classification (SRC) method serves as the classifier to recognize unknown faces. In the original work of SRC, the extended SRC (eSRC) scheme is presented to deal with occlusions, which has very heavy computational cost. The experimental results show that our method can achieve good recognition accuracy and has much lower computational cost than eSRC.     

A palmprint recognition method based on multi-step representation

Sparse representation uses all training samples to represent a test sample only once, which can be regarded as a one step representation. However, in palmprint recognition, the appearances of palms are highly correlated which means the information provided by all the training samples are redundant while using the representation-based methods. Hence, how to obtain suitable samples for representation deserves exploring. In this paper, we devise a multi-step representation manner to extract the most representative samples for representation and recognition. In addition, the proposed sample selection strategy is based on contributions of the classes, not merely the effort of a single sample. Compared with some other appearance-based methods, the proposed method obtained a competitive result on PolyU multispectral palmprint database.     

A sparse representation method based on kernel and virtual samples for face recognition

To improve the classification accuracy of face recognition, a sparse representation method based on kernel and virtual samples is proposed in this paper. The proposed method has the following basic idea: first, it extends the training samples by copying the left side of the original training samples to the right side to form virtual training samples. Then the virtual training samples and the original training samples make up a new training set and we use a kernel-induced distance to determine M nearest neighbors of the test sample from the new training set. Second, it expresses the test sample as a linear combination of the selected M nearest training samples and finally exploits the determined linear combination to perform classification of the test sample. A large number of face recognition experiments on different face databases illustrate that the error ratios obtained by our method are always lower more or less than face recognition methods including the method mentioned in Xu and Zhu [21], the method proposed in Xu and Zhu [39], sparse representation method based on virtual samples (SRMVS), collaborative representation based classification with regularized least square (CRC_RLS), two-phase test sample sparse representation (TPTSSR), and the feature space-based representation method.     

Quaternion-based improved LPP method for color face recognition

In recent years, pattern recognition and computer vision have increasingly become the focus of research. Locality preserving projection (LPP) is a very important learning method in these two fields and has been widely used. Using LPP to perform face recognition, we usually can get a high accuracy. However, the face recognition application of LPP suffers from a number of problems and the small sample size is the most famous one. Moreover, though the face image is usually a color image, LPP cannot sufficiently exploit the color and we should first convert the color image into the gray image and then apply LPP to it. Transforming the color image into the gray image will cause a serious loss of image information. In this paper, we first use the quaternion to represent the color pixel. As a result, an original training or test sample can be denoted as a quaternion vector. Then we apply LPP to the quaternion vectors to perform feature extraction for the original training and test samples. The devised quaternion-based improved LPP method is presented in detail. Experimental results show that our method can get a higher classification accuracy than other methods.     

高效率光参量转换

用一块6.5×9×20(长度)mm KTP晶体获得625nm～3575nm的皮秒可调谐激光.能量转换效率高达30%,最大信号光峰值功率34MW.     

Improve the two-phase test samples representation method for palmprint recognition

Palmprint recognition, as a very important personal identification technology, is taking more and more attention. A recently proposed method – two-phase test samples representation method (TPTSR) has attracted much attention and performed very well in biometrics. The TPTSR not only is a competent representation-based classification method, but also is computationally much more efficient than the original sparse representation methods. However, though the TPTSR seems to be suitable for palmprint recognition, it has not been widely tested and it is not known how to properly set the parameter (the number of the nearest neighbors), which is definitely crucial for real-world applications. This paper will analyze the performance of the method in the palmprint identification for the first time and explore the proper value of the parameter of the method. In order to address the above issues, lots of experiments on the palmprint recognition are conducted. This paper also shows experimental comparisons between TPTSR and several other methods. This paper provides significant instructions apply TPTSR to palmprint recognition.     

基于场匹配法的双排矩形栅慢波结构高频特性研究

本文运用场匹配法对具有任意位错的双排矩形栅慢波结构的场分布、色散特性及耦合阻抗进行了研究．研究结果表明,场匹配法推导的色散特性与仿真软件CST和HFSS计算的结果完全一致,耦合阻抗介于CST和HFSS之间．在此基础上,详细研究了上下两排系统之间位错对色散特性及耦合阻抗的影响．当位错严格为半个周期时,第一阻带消失,第一个模式最高截止频率与第二个模式最低截止频率重叠,发生简并;当位错为0．45倍周期时,在保证耦合阻抗不变的情况下,基模的通带虽降低了2．8GHz,但阻带却增大了7．9GHz,从而可以有效避免简并及模式竞争的发生．     

一种高效率和高功率的双同轴虚阴极振荡器

 提出了一种双同轴虚阴极振荡器，并对其进行了理论分析和数值模拟。这种振荡器采用了一种新的能量提取结构，将波束相互作用和能量提取分开进行。提取区内轴的左端面可以反射微波，为波束相互作用提供反馈机制；同时还可以吸收在下游漂移的电子，这有利于输出功率和效率的提高。在器件的入口处注入峰值电压为500kV的梯形脉冲时，模拟得到了瞬时峰值功率大于2.5GW，周期平均的峰值功率约1.2GW的微波输出，频率为2.175GHz，能量提取效率达到11％。输出的微波保持了传统同轴虚阴极振荡器的优点，模式纯度高、谱宽非常窄。     

相控阵高强度聚焦超声高阵元驱动效率相位独立控制方法

针对如何简化上百阵元高强度聚焦超声相控阵控制模块的问题,本文提出高阵元驱动效率的相位独立控制方法。利用该方法计算了一种256阵元中心开孔球面相控阵的聚焦声场,并与传统的幅度相位综合控制方法进行了分析和比较。计算和实验结果表明该方法在简化相控阵驱动控制模块、提高阵元驱动效率的同时,可以有效实现相控阵的空间三维单焦点聚焦。由于阵元驱动控制的简化而引起的焦域能量偏差,可以通过适当调整相控阵输入总声功率进行有效的补偿。     

Active polarization imaging system to discriminate adaptively with diagonal Mueller matrix

A promising method to optimize the polarization state of two-channel active polarization imaging system is presented. In this method, it is seminal that the detecting function of the imaging system is regarded as a discriminant projection of the observed objects’ polarization features (elements of the Mueller matrix). The polarization state can be seen as a physical classifier which can be obtained by training samples. The image acquired with the system that has the designed optimal polarization state become discriminative results directly. The effectiveness of the proposed method and the discriminative ability of the optimal polarization state are demonstrated by the experimental results.