Description: 极小边际熵等价于叉四阶累积量的平方和最小。通过迭代使四阶累积矩阵对角化，实现交叉四阶累积量的平方和的极小化。他是语音识别的重要预处理算法-minimum entropy equivalent to the marginal four bands fork cumulative amount of square and smallest. Through iterative four bands so that the cumulative matrix diagonalization, four bands to achieve cross-cumulative amount of square and the minimization. He is an important voice recognition algorithm pretreatment Platform: |
Size: 3526 |
Author:韩仲志 |
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Description: 模式识别中关于
LMSE最小均方误差算法
一中算法-pattern recognition on the wan minimum mean square error algorithm an algorithm Platform: |
Size: 1806 |
Author:任大 |
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Description: 用C编写基于凌阳SPCE061A芯片的图象识别模块可实现（1）获得数据并处理。（2）控制按扭KEY1，播放物体的形状和颜色。（3）分辩的颜色：红色、绿色、蓝色、黄色。（4）分辩的形状：正方形、长方形、圆形、三角形。（5）控制按键KEY2，小车跟着红色的物体移动，别的颜色不做跟踪。-prepared based on the C Sunplus SPCE061A chip image recognition module can be realized (1) access to data and handled. (2) the control buttons KEY1 broadcast the shape of the object and color. (3) differentiate colors : red, green, blue and yellow. (4) distinguish shapes : square, rectangular, circular, triangular. (5) KEY2 control buttons, a red car with mobile objects, not another color tracking. Platform: |
Size: 23608 |
Author:三石 |
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Description: 极小边际熵等价于叉四阶累积量的平方和最小。通过迭代使四阶累积矩阵对角化，实现交叉四阶累积量的平方和的极小化。他是语音识别的重要预处理算法-minimum entropy equivalent to the marginal four bands fork cumulative amount of square and smallest. Through iterative four bands so that the cumulative matrix diagonalization, four bands to achieve cross-cumulative amount of square and the minimization. He is an important voice recognition algorithm pretreatment Platform: |
Size: 3072 |
Author: |
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Description: 模式识别中关于
LMSE最小均方误差算法
一中算法-pattern recognition on the wan minimum mean square error algorithm an algorithm Platform: |
Size: 2048 |
Author:任大 |
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Description: 用C编写基于凌阳SPCE061A芯片的图象识别模块可实现（1）获得数据并处理。（2）控制按扭KEY1，播放物体的形状和颜色。（3）分辩的颜色：红色、绿色、蓝色、黄色。（4）分辩的形状：正方形、长方形、圆形、三角形。（5）控制按键KEY2，小车跟着红色的物体移动，别的颜色不做跟踪。-prepared based on the C Sunplus SPCE061A chip image recognition module can be realized (1) access to data and handled. (2) the control buttons KEY1 broadcast the shape of the object and color. (3) differentiate colors : red, green, blue and yellow. (4) distinguish shapes : square, rectangular, circular, triangular. (5) KEY2 control buttons, a red car with mobile objects, not another color tracking. Platform: |
Size: 23552 |
Author:三石 |
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Description: 模式识别 均值 该方法取定C个类别和选取C个初始聚类中心，按最小距离原则将各模式分配到C类中的某一类，之后不断地计算类心和调整各模式的类别，最终使各模式到其判属类别中心的距离平方之和最小。-Pattern Recognition mean the Methods set C categories and select the C of the initial cluster centers, according to the principle of minimum distance between each pattern assigned to a class C category, followed by constantly calculating the type of heart and adjust the model category, ultimately enable the model to its sub category is the square of the distance between centers and the smallest. Platform: |
Size: 279552 |
Author:tengfang |
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Description: LMS滤波器示例程序，在TURBOC中运行
这是一个简单的可图形显示的C程序
输入信号是一个被噪声污染了的sin信号。 */
/* 运行后，屏幕的上方是输入信号，下方是经过LMS滤波后的输出信号
-LMS filter sample programs, run in TurboC This is a simple graphical display can process the C input signal is a noise contaminated signal sin.*//* Running, the top of the screen is the input signal, the bottom is the result after the LMS filter output signal Platform: |
Size: 4096 |
Author:蜗牛 |
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Description: 为了更准确地识别人的表情，在识别人脸7 种基本表情（愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶）时，采用了局域二值
模式技术提取面部特征，进行由粗略到精细的表情分类。在粗略分类阶段，7 种基本表情中的2 种表情被选为初步分类结果（候选表情）。
在精细分类阶段，选用计算加权卡方值确定最终分类结果。采用日本的Jaffe 表情数据库来验证算法性能，对陌生人表情的识别率为77.9％，
其结果优于采用同样数据库的其他方法，且易于实现-In order to more accurately identify the person s facial expressions, in the identification of seven kinds of basic facial expressions (anger, disgust, fear, happy, expressionless, sadness and surprise), the use of local binary pattern of facial features extraction techniques, carried out by the rough to the fine expression classification. In the rough classification stage, the seven kinds of basic expressions in the two kinds of expression was selected as the initial classification results (candidate expressions). In the fine classification stage, the choice of calculating the weighted chi-square value to determine the final classification results. Jaffe expressions used in Japan to validate algorithm performance database of strangers face recognition rate was 77.9, the result is better than using the same database in other ways, and are easy to achieve Platform: |
Size: 212992 |
Author:张波 |
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Description: 另一种增量人脸识别算法——参考文献“Application for Face Recognition. Haitao Zhao,Pong Chi Yuen,and T.Kwok.”-Another incremental face recognition algorithms- References Application for Face Recognition. Haitao Zhao, Pong Chi Yuen, and T. Kwok. Platform: |
Size: 2048 |
Author:陈静 |
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Description: 小波变换在信号处理、图像处理、模式识别、计算机视觉等
方面广泛应用。本文提出了一种基于Haar小波的时问序列相似
模式匹配模型．它首先对时间序列数据进行Haar小波变换．以
降低数据的维度。首先将所要查询的时间序列进行降维，也就是
对时间序列数据进行小波变换，对序列进行标准化131，得到降维
之后的小波序列．这样可以得到系数子集的良好近似采用尺度
序列表示原始序列．并将其看成多维空间中的一个点，通过计算
两序列差的平方和的平方根作为这两个时间序列的距离函效
(即欧氏距离)。用小波变换保持了局部性质。而欧氏距离就是要
判断相似性的距离函数．所以当在作完变换之后。通过低频分量
来压缩数据，成功的计算出实际距离的下界。如果计算的结果小
于一个由用户所定义的门槛值．则认为这两个时问序列是相似
的。
在-Of wavelet transform in signal processing, image processing, pattern recognition, computer vision, etc.
Aspects of wider application. In this paper, Haar wavelet-based time series similarity
Pattern matching model. It is first time-series data, Haar wavelet transform. To
To reduce the data dimension. Will first want to check the time series dimension reduction, that is,
Pairs of time series data using wavelet transform, standardize the sequence 131, to be reduced-
After the wavelet sequence. This can be a good approximation coefficients of a subset of the use of scale
Sequence indicated that the original sequence. And to treat it as a multi-dimensional space a point, by calculating the
Two sequences and the square root of the square of the difference as the distance between these two time series letter effect
(Ie, Euclidean distance). Maintained with the local nature of the wavelet transform. The Euclidean distance is to
To determine simila Platform: |
Size: 323584 |
Author:lx |
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Description: 为了更有效地提取图像的局部特征，提出了一种基于2维偏最小二乘法(two—dimensional partial least
square，2DPLS)的图像局部特征提取方法，并将其应用于面部表情识别中。该方法首先利用局部二元模式(1ocal
binary pattern，LBP)算子提取一幅图像中所有子块的纹理特征，并将其组合成局部纹理特征矩阵。由于样本图像
被转化为局部纹理特征矩阵，因此可将传统PLS方法推广为2DPLS方法，用来提取其中的判别信息。2DPLS方法
通过对类成员关系矩阵的构造进行相应的修改，使其适应样本的矩阵形式，并能体现出人脸局部信息重要性的差
异。同时，对于类成员关系协方差矩阵的奇异性问题，也推导出了其广义逆的解析解。基于JAFFE人脸表情库的
实验结果表明，该方法不但可以有效地提取图像局部特征，并能取得良好的表情识别效果。-To better the image of the local feature extraction, a partial least squares method based on 2D (two-dimensional partial least
square, 2DPLS) image local feature extraction method, and applied to facial expression recognition. In this method, use of local binary pattern (1ocal
binary pattern, LBP) operator extracts an image texture features of all sub-blocks, and their combination into the local texture feature matrix. As the sample image
Be translated into the local texture feature matrix, so the traditional PLS method can be generalized to 2DPLS method used to extract the identification information. 2DPLS method
Through the class membership matrix in the corresponding modifications to adapt the sample matrix, and can reflect the importance of face poor local information
Different. Meanwhile, members of the class covariance matrix of the singular relations issues, also derived the generalized inverse of the analytical solution. Based on the JAFFE facial expression database
Platform: |
Size: 315392 |
Author:MJ |
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Description: Performs Procustes point alignment on a group of point sets. Method rigidly aligns, shifts, and scales points to reduce mean square error.
Method is described in:
B. Klare, P Mallapragada, A.K. Jain, and K. Davis, "Clustering Face Carvings: Exploring the Devatas of Angkor Wat", in Proceedings International Conference on Pattern Recognition (ICPR), 2010.
http://www.cse.msu.edu/~klarebre/docs/ICPR_AW.pdf-Performs Procustes point alignment on a group of point sets. Method rigidly aligns, shifts, and scales points to reduce mean square error.
Method is described in:
B. Klare, P Mallapragada, A.K. Jain, and K. Davis, "Clustering Face Carvings: Exploring the Devatas of Angkor Wat", in Proceedings International Conference on Pattern Recognition (ICPR), 2010.
http://www.cse.msu.edu/~klarebre/docs/ICPR_AW.pdf Platform: |
Size: 1024 |
Author:B |
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Description: 高独特性特征的选择以及合适匹配策略的选用是人脸识别技术的关键。讨论了基于仿射不变的几何特
征SIFT算子进行人脸识别的方法。SIFT算子的计算复杂度较高，并且不同的人脸表情和图像模糊会加大特征匹
配的难度。为克服上述缺点，提出了一种新的算法，将选择6个人脸上感兴趣子区域进行描述，并根据各自的独特
性赋予不同的权值，最后在匹配过程中使用相似度的平方来减小偏差数据造成的影响。实验结果表明，该方法能
有效减轻表情变化对于身份识别率急剧下降的影响，并可显著减少计算复杂度和特征匹配时间。-Choosing a distinctive feature and matching criterion is key to developing a reliable face recognition system．
This paper discusses the availability of one of geometric feature invariants，scale invariant feature transform (SIFT)
descriptor based face recognition． The SIFT feature description of an image is typically complex． In most cases， the
difficulty of feature matching problem is aggravated when the diferent face expressions and image blur exist． For
abovementioned issues，in this paper we proposes a new method that six interest sub—regions from the face are selected to be
described and later be calculated through diferent weights according to their distinctiveness．The square of the similarity is
used to solve the problem of data deviation．The experimental results demonstrate that our method does effectively moderate
the face expression efect． It also successfully reduces the complexity and matching time of SIFT feature sets． Platform: |
Size: 286720 |
Author:陈方芳 |
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Description: 本程序采用一种基于垂直Sobel算子检测边缘和投影法的车牌定位方法,根据车牌区域竖直纹理突出的特点，利用Sobel垂直算子提取边缘，然后投影得到车牌的水平投影图，利用水平投影图分割出车牌，再运用膨胀运算进行车牌垂直定位。利用旋转投影法寻找车牌倾斜角度，然后用双线性插值进行车牌图像倾斜矫正。对经过精定位的车牌利用垂直投影法，找出各个字符的中心点，根据字符中心点位置进行切割。同时将切割出来的图片与数据库中的每张模板图片进行减法运算，计算该图片与模板的均方误差，其均方误差最小的模板即为识别出的字符。-This program uses edge detection and projection of license plate positioning method based on vertical Sobel operator, according to a prominent feature of the license plate area vertical texture operator Sobel vertical edge extraction, then the horizontal projection of the projector to get the license plate, the horizontal projection the Figure split out the license plate, and then use the the dilation operation license plate vertical positioning. Take advantage of the rotation projection method to find the license plate tilt angle, and then the license plate image tilt correction bilinear interpolation. The focal point for fine positioning license plate using the vertical projection method to identify each character, according to the the character center point cut. Cutting out pictures with each template images in the database, subtraction, calculation of the mean square error of the picture with the template, the mean square error of the smallest template is identified characters. Platform: |
Size: 7168 |
Author:埃米尔 |
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Search - square recognition