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Description: 先用C-均值聚类算法程序，并用下列数据进行聚类分析。在确认编程正确后，采用蔡云龙书的附录B中表1的Iris数据进行聚类。然后使用近邻法的快速算法找出待分样本X（设X样本的4个分量x1=x2=x3=x4=6；子集数l=3）的最近邻节点和3-近邻节点及X与它们之间的距离。-First C-means clustering algorithm procedures and with the following data for cluster analysis. After confirming the correct programming, using the book蔡云龙Table 1 in Appendix B of the Iris data clustering. And the use of a close neighbor of law to be fast algorithm to find sub-samples of X (for X samples four components x1 = x2 = x3 = x4 = 6 subset of the number of l = 3) of the nearest neighbor nodes and 3- neighbor nodes and X and the distance between them.
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Size: 1024 |
Author: jack |
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Description: 自己编写的一个比较简单的眼睛虹膜定位的算法。-I have written a relatively simple eye iris location algorithms.
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Size: 2129920 |
Author: 饶丹 |
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Description: 使用分解聚类算法在IRIS数据上进行聚类分析，IRIS数据是由鸢尾属植物的三种单独的花的测量结果所组成，模式类别数为3，特征维数是4，每类各有50个模式样本，总共有150个样本。-The use of decomposition in the IRIS data clustering algorithm on the cluster analysis, IRIS data are from the iris flower three separate components of the measurement results, models for category 3, 4 are characteristic dimension, of each type of each 50 model samples, a total of 150 samples.
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Size: 3072 |
Author: liz |
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Description: 使用c-均值聚类算法在IRIS数据上进行聚类分析，随机选择三个初始聚类中心，经过多次迭代，最终将150个样本分为三类。-Use c-means clustering algorithm in the IRIS data on the cluster analysis, three randomly chosen initial cluster centers, through a series of iterative, 150 samples will eventually fall into three categories.
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Size: 3072 |
Author: liz |
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Description: Corner detection for Iris-Corner detection for Iris..
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Size: 368640 |
Author: Guru |
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Description: 基于NesC/TinyOS的无线传感器网络测量节点源码，平台：Crossbow IRIS，按用户指定时间间隔读取Decagon公司的EC5和EC-TM传感器信息-Based on NesC/TinyOS wireless sensor network node measurement source, platform: Crossbow IRIS, according to user-specified time interval to read Decagon s EC5 and EC-TM sensor information
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Size: 105472 |
Author: LI, Zhen |
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Description: Iris使用详解—基础篇-命名为Iris1.rar,同时还上传了Iris使用详解—提高篇，命名为Iris2.rar。希望能对大家有所帮助。呵呵。-Iris Detailed use- basic chapter- named Iris1.rar, but also explain the use of uploaded Iris- improve articles, named Iris2.rar. I hope everyone can help. Ha ha.
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Size: 265216 |
Author: 454545 |
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Description: Iris使用详解—基础篇-命名为Iris1.rar,同时还上传了Iris使用详解—提高篇，命名为Iris2.rar。希望能对大家有所帮助。呵呵。-Iris Detailed use- basic chapter- named Iris1.rar, but also explain the use of uploaded Iris- improve articles, named Iris2.rar. I hope everyone can help. Ha ha.
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Size: 185344 |
Author: 454545 |
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Description: code for iris recognition
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Size: 3304448 |
Author: sharad |
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Description: 虹膜定位的目的在于根据给定的人眼图像，定位出虹膜区域。-The purpose of positioning the iris according to a given image of the human eye, positioning the region of the iris.
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Size: 65536 |
Author: 傅强 |
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Description: 精确定位 虹膜的内外边缘，并分割出虹膜。-iris segmention。
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Size: 12288 |
Author: 徐秀丽 |
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Description: Bayes分类器应用于IRIS数据集的例子-An example of Bayes Classifier applied on IRIS Data Set
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Size: 45056 |
Author: LikeTheBird |
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Description: Fisher线性判别分类器应用于IRIS数据集的例子-An example of Fisher linear discriminant Classifier applied on IRIS Data Set
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Size: 164864 |
Author: LikeTheBird |
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Description: RBF神经网络应用于IRIS数据集的例子-Am example of RBF-NN applied on IRIS Data Set
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Size: 19456 |
Author: LikeTheBird |
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Description: another Skin for Iris Skins that will add bueaty to your application
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Size: 214016 |
Author: Mohammad |
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Description: Skin file for Iris Skins that will add bueaty to your application
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Size: 6144 |
Author: Mohammad |
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Description: isodata算法，针对的是iris数据库，代码可以直接运行，很好用。要是要用于自己的数据库，只需要把文件名，结构体信息修改下即可。-isodata algorithm, the iris against a database, code can be run, it just works. If the database to be used for their own, just to the file name, the structure of information can be modified.
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Size: 1065984 |
Author: 周丽霞 |
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Description: 在数字图像中虹膜位置的有效定位是虹膜识别的关键问题。用一种基于主动轮廓线模型的方法定位虹膜的位置，先用灰度投影法检测出瞳孔内的一点作为瞳孔的伪圆心，该圆心只要能落在瞳孔内部即可。然后以该伪圆心为中心，在其周围等角度间隔地取N个点作为初始的snake基准点，按照snake 的运行机制不断进化，直到虹膜的内边界为止。最后，计算进化后的snake形心和snake上的控制点与该形心的距离，取其平均值作为瞳孔的半径，动态轮廓模型的形心作为瞳孔的圆心，即可准确定位出虹膜内边界的位置。实验表明，与常见的定位方法相比，文中的方法速度快、精度高，而且，对瞳孔初始的伪圆心要求不高，鲁棒性更强。-Digital image of the iris in the location of the effective positioning of the key issues of iris recognition. With a model based on the active contour method of positioning the location of the iris, the first gray projection method used to detect the pupil as the point of the pseudo-pupil center, the center of a circle as long as it can fall within the pupil. And then to the pseudo-center as the center, in terms of their spacing around the N points to get the snake as the initial reference point, the operating mechanism in accordance with the snake continued to evolve until the border until the inner iris. Finally, after calculating the evolution of snake and snake-shaped heart of the control point on the centroid distance, choose the average as the radius of the pupil, the dynamic contour model of the centroid as the center of pupil can be an accurate positioning within the borders of the iris position. The experimental results show that the position with common methods, the methods o
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Size: 1764352 |
Author: 侯汶杉 |
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Description: feature selection from iris data set it will use statistical methods and get the best set of features then use graphs to classify the data-feature selection from iris data set it will use statistical methods and get the best set of features then use graphs to classify the data
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Size: 28672 |
Author: madurangak |
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Description: Neural Network Application. Iris Dataset and Wine Dataset.
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Size: 1064960 |
Author: polash |
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