perfect matching

perfect matching的基本解释

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全匹配

与perfect matching相关的网络例句 [注：此内容来源于网络，仅供参考]

On the base of finishing image matching, Aiming at the fact that sequence image matching and binocular stereo image matching simultaneously exists in three dimensions movement analyses based on point feature, The method of double matching restrict combining sequence and stereo image matching is brought forward in matching distinctness stage after researching and anglicizing the character of sequence matching and stereo image matching in processing.

在完成图象匹配的基础上，针对基于点特征的三维运动分析中，同时存在序列、立体图象匹配问题，研究分析该过程不同匹配的特点，提出了序列、立体匹配相结合，分阶段的双匹配约束方法。

Just a perfect day Drink sangria in the park And then later, when it gets dark, we'll go home Just a perfect day Feed animals in the zoo Then later a movie too, and then home Oh it's such a perfect day I'm glad I spent it with you Oh such a perfect day You just keep me hanging on You just keep me hanging on Just a perfect day Problems all left alone Weekenders on our own It's such fun Just a perfect day You make me forget myself I thought I was someone else Someone good Oh it's such a perfect day I'm glad I spent it with you Oh such a perfect day You just keep me hanging on You just keep me hanging on You're going to reap just what you sow You're going to reap just what you sow You're going to reap just what you sow You're going to reap just what you sow

完美的一天在公园喝着sandria 过了一会儿，当天渐渐黑了，我们也将要回家了完美的一天再动物园喂着动物接着看电影，回家哦，今天是多么完美的一天啊我非常高兴今天是和你一起度过的哦，多么完美的一天你和我一直在一起你和我一直在一起完美的一天把所有问题抛在脑后度过一个个愉快的周末是多么有趣完美的一天你让我忘记了我自己我想我是另外一种人一种愉快的人哦，今天是多么完美的一天啊我非常高兴今天是和你一起度过的哦，多么完美的一天你和我一直在一起你和我一直在一起

It solves the problem that the unitary contour presentation can not correctly extract face contour in a face image which suffers from scale, rotation etc. The definition of the internal and external energy function is provided. At the same time, the global matching algorithm and local matching algorithm is given. The experiment shows that this presentation and the accompanying matching algorithm can be used to extract the face contour very well. So the image segmentation can be implemented by using it.②By analyzing the recognition principle of PCA method, we can conclude that the face images coming from different surrounding consist of different face image space. This is the essential reason that makes the generality of PCA method worse. Also, we give a measurement means to measure the distance from different face image space, so we can analyze face image space more conveniently.③We also construct various scale models and rotation pose models to detect the scale and rotating angle of face image to be recognized. The experiment results show that the detecting precision is very high. So it is good for face image feature extraction and face image representation.④Similarly, we construct local feature models of face image and utilize them to detect the local feature of face image. At the same time, we put forward a novel face image local feature detection algorithm, locating step by step. The experiment results show that this method can accurately detect the location of local face feature in a image.⑤A novel face image presentation model, dual attribute graph , is put forward. Firstly, it utilizes attribute graph to present the face image, then exact the local principal component coefficient and Gabor transform coefficient of thc pixels which corresponds to the nodes of the graph as the attribute of the nodes. This representation fully makes use of the statistical characteristic of the local face feature and utilizes Gabor transform to present the topographical structure of face image. So DAG has more general property.⑥Based on the DAG presentation, we give a DAG matching function and matching algorithm. During the design of the function and algorithm, the noise factor, e. g., lighting, scale and rotation pose are considered and tried to be eliminated. So the algorithm can give more general property.⑦A general face image recognition system is implemented. The experiment show the system can get better recognition performance under the noise surrounding of lighting, scale and rotation pose.

本文在上述研究的基础上，取得了如下主要研究成果：①构造了一个通用的人脸轮廓模型表示，解决了由于人脸图象尺度、旋转等因素而使得仅用单一轮廓表示无法正确提取人脸轮廓的问题，并给出了模型内、外能函数的定义，同时给出了模型的全局与局部匹配算法，实验表明，使用这种表示形式以及匹配算法，能够较好地提取人脸图象的轮廓，可实际用于人脸图象的分割；②深入分析了PCA方法的识别机制，得出不同成象条件下的人脸图象构成不同的人脸图象空间的结论，同时指出这也是造成PCA方法通用性较差的本质原因，并给出了不同人脸空间距离的一种度量方法，使用该度量方法能够直观地对人脸图象空间进行分析；③构造了各种尺度模板、旋转姿势模板以用于探测待识人脸图象的尺度、旋转角度，实验结果表明，探测精确度很高，从而有利于人脸图象特征提取，以及图象的有效表示；④构造了人脸图象的各局部特征模板，用于人脸图象局部特征的探测；同时提出了一种新的人脸图象局部特征探测法---逐步求精定位法，实验结果表明，使用这种方法能够精确地得到人脸图象各局部特征的位置；⑤提出了一种新的人脸图象表示法---双属性图表示法；利用属性图来表示人脸图象，并提取图节点对应图象位置的局部主成分特征系数以及Gabor变换系数作为图节点的属性，这种表示方法充分利用了人脸图象的局部特征的统计特性，并且使用Gabor变换来反映人脸图象的拓扑结构，从而使得双属性图表示法具有较强的通用性；⑥在双属性图表示的基础上，给出双属性图匹配函数及匹配算法，在函数及算法设计过程中，考虑并解决了光照、尺度、旋转姿势变化等因素对人脸图象识别的影响，使得匹配算法具有较强的通用性；⑦设计并实现了一个通用的人脸图象识别系统，实验结果表明，该系统在图象光照、尺度、旋转姿势情况下，得到了较好的识别效果。

与perfect matching相关的网络解释 [注：此内容来源于网络，仅供参考]

perfect matching：完美匹配

最多边的匹配.匹配数(matching number)是最大匹配的大小.完美匹配(perfect matching)是匹配了所有点的匹配.完备匹配(complete matching)是匹配了二分图较小部份的所有点的匹配.从若干可能的安排或方案中寻求某种意义下的最优安排或方案,

perfect matching：全匹配

perfect mapping 完全映射 | perfect matching 全匹配 | perfect number 完全数

perfect matching：线图

线图:Perfect matching | 配矿:ore matching | 配合:Matching

Perfect matching problem：完美匹配问题

最佳离散信号:perfect discrete signal | 完美匹配问题:Perfect matching problem | 抵押权完善:Mortgage the power is perfect