小样本训练集下人脸图像超分辨率重构算法

文章提出了一种针对小样本训练集的人脸图像超分辨率重构算法.该算法在马尔可夫网络模型的基础上,将视觉相容性检查准则引入到候选分块的匹配准则中,提高了匹配精度和块间兼容性.将权值机制融合进图像的重构过程,强化了训练集的有用性.实验表明,在小样本训练集下,算法能有效地抑制块效应和局部失真现象,明显改善超分辨率图像的质量.     

基于张量投票的图像超分辨率算法

张量投票算法在提取图像主观轮廓上具有良好的效果。文章提出了一种基于张量投票的图像超分辨率算法。首先,用二维张量矩阵存储低分辨率图像各像素点所处的位置特征信息,并利用稀疏张量投票将特征信息进行加强,再使用稠密张量投票产生高分辨率图像对应的二维张量矩阵,此张量矩阵包含了视觉特性强的边缘信息,最后利用该边缘信息指导高分辨率图像的重构。实验结果表明,该方法得到的高分辨率图像信噪比高、视觉效果好。     

基于张量投票的图像超分辨率算法

张量投票算法在提取图像主观轮廓上具有良好的效果。文章提出了一种基于张量投票的图像超分辨率算法。首先,用二维张量矩阵存储低分辨率图像各像素点所处的位置特征信息,并利用稀疏张量投票将特征信息进行加强,再使用稠密张量投票产生高分辨率图像对应的二维张量矩阵,此张量矩阵包含了视觉特性强的边缘信息,最后利用该边缘信息指导高分辨率图像的重构。实验结果表明,该方法得到的高分辨率图像信噪比高、视觉效果好。     

多帧超分辨率图像重建迭代小波算法研究

为了解决由于物体的运动和拍摄环境等因素,造成拍摄后多帧图像分辨率低和细节丢失的问题。本文对现有超分辨图像处理算法查阅研究,提出超分辨率图像重建迭代小波算法。最终对比迭代小波算法运算后最终的图像和参考帧图像,并比较图像信噪比的变化得出迭代小波算法的优越性。     

视频图像超分辨率应用系统设计与实现

文章设计实现了一个视频图像超分辨率应用系统,描述了超分辨率实验平台的设计,包括系统结构、图像与视频内容、主要算法模块设计,介绍了开发该系统的平台、开发该平台所使用的关键技术,给出了使用本系统处理后的图像,并对处理后的图像进行了主观评价。     

一种基于小波神经网络的超分辨图像重构

针对在航空航天遥感领域中使用的CCD相机对景物进行成像时,由于像元尺寸的限制导致图像低分辨率的问题和传统的图像重构算法的不足,提出了小波神经网络映射图像重构过程非线性特性的方法。实验表明,用小波神经网络对低分辨的图像重构,可以得到高分辨率图像。通过仿真实验得到高分辨率和高信噪比的超分辨结果：     

基于SSE指令集的运动目标模板匹配算法设计

本文针对序列图像中运动目标识别的广泛需求,分析了模板匹配算法的实现过程及特点,研究、设计了一种基于SSE指令集的运动目标模板匹配算法。该算法利用SSE4指令集实现多位数据并行计算,显著提高了模板匹配算法的处理速度。实验结果表明,该算法对于序列图像中的运动目标识别具有速度快、识别精度高的特点,具有一定的应用价值。     

基于邻域搜索的快速分形图像编码算法

文章在基本分形图像压缩算法的基础上提出了一种基于邻域搜索的快速算法。将子块按标准差分为平滑块和非平滑块,对于平滑子块直接存储其均值,以减少需要搜索匹配父块的子块数;对于非平滑子块,在从其周边邻域内划分得到的父块中搜索最优匹配父块,并引入自适应误差阈值和扩大邻域范围搜索的最大次数来控制子块搜索的邻域范围。实验证明,该算法编码速度大大提高。     

基于meanshift超像素CRF模型的高分辨率遥感图像分类

高分辨率遥感图像中的纹理信息和光谱信息非常丰富,提出通过mean shift算法将遥感图像过分割成小的区域,即为超像素,并且以这些超像素为节点,建立CRF模型进行参数估计和推导。结果表明:该方法将像素变成了超像素极大地减少了遥感图模型中节点的数目,简化了计算模型,超像素较好地保留图像的内在结构信息,得到了较好的分类结果。     

基于LapSVM的物流编号图像自动识别算法

根据目前物流编号识别效率和精度低的缺陷,提出一种基于图像处理和Laplacian支持向量机(Lap SVM)的物流编号自动识别算法。该算法通过图像采集设备获得物流编号图像,对图像进行预处理和数字字符分割,接着对各个数字字符图像进行特征提取,同时利用标记样本与未标记样本来训练支持向量机(SVM)分类器,实现很少标记样本下的精确字符分类,从而达到编号识别的目的。     

低空无人机遥感影像高精度自动匹配方法研究

本文针对低空无人机遥感影像的特点,提出一种基于Harris角点和SIFT特征的自动匹配算法,并利用基于单应矩阵和核线几何双重约束的RANSAC相对定向剔除错误匹配。通过一组影像的自动匹配试验,结果具有较高的匹配正确率,验证了算法的有效性,可为区域网平差解算提供良好基础。     

A modified deferred acceptance algorithm for many-to-one matching markets with externalities among firms

We consider a many-to-one matching market with externalities among firms where each firm’s preferences satisfy substitutability, increasing choice and no external effect by unchosen workers, which are defined by  Bando (2012). We first illustrate that a sequential version of the deferred acceptance (DA) algorithm with worker-proposing may not find a worker-optimal quasi stable matching. Then, we provide a modified DA algorithm in which (i) each worker simultaneously proposes to his most preferred firm that has not rejected him and (ii) each firm chooses its acceptable workers from the cumulative set of workers who have ever proposed to it, assuming that the other workers proposing to its rival firms are hired. We show that this algorithm finds a worker-optimal quasi stable matching. We also show that this algorithm can be generalized into a fixed point algorithm.     

Agreement toward stability in matching markets

For firms with responsive preferences, we prove that the set of stable matchings unanimously preferred by workers to any firm quasi-stable matching has a lattice structure. This follows from a generalization of the Decomposition Lemma. The result does not hold when firms have q-substitutable preferences. Nevertheless, we show that the set of stable matchings unanimously preferred by workers to a firm quasi-stable matching contains an element which is unanimously least preferred by workers, and most preferred by firms. When a firm quasi-stable matching is fed into our extension of the Deferred Acceptance algorithm (where firms propose), the existence of this matching guarantees the success of the algorithm.     

入侵检测系统的融合模式匹配算法

运用入侵检测系统的融合模式匹配算法取得待匹配的字符串集合的公共前缀与文本匹配,若为单模式匹配则匹配结束,若为多模式匹配则将文本被分割成的字符串集中的字符串分别与模式串匹配,该融合算法对单模式匹配和多模式匹配均提高了效率,识别了正文的大小写。     

Assessing the performance of matching algorithms when selection into treatment is strong

This paper investigates the method of matching regarding two crucial implementation choices: the distance measure and the type of algorithm. We implement optimal full matching—a fully efficient algorithm—and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong selection into treatment and treatment effect heterogeneity a full matching seems preferable. If heterogeneity is weak, pair matching suffices. Copyright © 2007 John Wiley & Sons, Ltd.     

SIFT算法的介绍和应用

SIFT算法是众多图像匹配算法中被广泛应用的一种,其具有良好的尺度、光照和空间旋转不变等特征。文章介绍了SIFT算法的4个主要步骤,并将该算法应用到多色二值压缩算法中,提高了压缩效率,缩小了压缩后的存储空间。     

SIFT算法的介绍和应用

SIFT算法是众多图像匹配算法中被广泛应用的一种,其具有良好的尺度、光照和空间旋转不变等特征。文章介绍了SIFT算法的4个主要步骤,并将该算法应用到多色二值压缩算法中,提高了压缩效率,缩小了压缩后的存储空间。     

构建最小入侵检测特征集的贪心算法

文章通过贪心算法构建入侵检测特征串的最小特征集,并生成基于该最小特征集的确定有限状态自动机(DFA)。实验结果表明:该方法减少入侵检测特征的存储空间40%,提高了基于DFA模式匹配的入侵检测速度,有效提升了入侵检测软硬件系统的性能。     

Online doctor-patient dynamic stable matching model based on regret theory under incomplete information

With the development of the Internet era, online medical community is increasingly favored by patients and health care workers. How to make a reasonable doctor-patient matching and meet patients' relevant needs through online medical platform is the key to realizing effective allocation of medical resources. This paper proposes an online doctor-patient dynamic stable matching model to deeply investigate the online doctor-patient matching problem under incomplete information. Firstly, we assign the score to each doctor based on the patient's attribute expectations and the doctor's evaluation information. Considering the difficulty of obtaining complete information in real life and different attribute representations, we propose incomplete information filling methods based on collaborative filtering algorithm. To make the filling result more reasonable, we introduce probabilistic linguistic information entropy to process the incomplete information. Secondly, considering psychological behavior, we use regret theory to calculate patients' satisfaction with doctors. Finally, with the definitions of rational matching and stable matching, we propose a doctor-patient dynamic stable matching model. This model can solve the problems like a temporary appointment and private matching and ensure the stability of matching. In addition, the model proposed in this paper considers not only patients' but also the doctors' needs. The case of online doctor-patient matching is based on “haodf.com”, and through comparative analysis can prove the effectiveness and superiority of this method.