资源列表
CHENGXU2
- rbf神经网络函数拟合。径向基函数，由样本选出固定中心值和delta-RBF neural network function fitting.Radial basis function (RBF), fixed center value chosen by the sample and the delta
CHENGXU1
- 神经网络BP算法，学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小-Neural network BP algorithm, learning rule is to use the steepest descent method, by back propagation to constantly adjust the network weights and threshold, minimize the error sum of squares of t
CHENGXU3
- 神经网络SVM函数拟合，对于线性不可分的情况，通过使用非线性映射算法将低维输入空间线性不可分的样本转化为高维特征空间使其线性可分，从而使得高维特征空间采用线性算法对样本的非线性特征进行线性分析成为可能-SVM neural network function fitting, in the case of linear inseparable, through the use of nonlinear mapping algorithm will undivided linear sample l
CHENGXU4
- 神经网络感知器进行函数拟合，对每一段函数使用一个感知器分别进行训练拟合。在拟合时对于该段函数上的点(x,y)分别均匀选取该段函数上下两边的点作为输入样本对应+1，-1为教师-Perceptron neural network for function fitting for each function USES a perceptron training fitting respectively.In fitting for this function on the right point (x
CHENGXU5
- 神经网络SVM实现分类，采用高斯核，标准差经过试验，最终定在0.81。训练和测试样本在1到1000之间间隔取点，训练样本取奇数，测试样本取偶数，没有噪声-SVM neural network to realize classification, USES the gaussian kernel, the standard deviation after test, final set at 0.81.Training and testing samples in the interval bet
fbmc easy power
- fbmc easy power
SSIP2013-master
- 一种图像对比度增强算法，包含demo，和图像性能测试-An image contrast enhancement algorithms, including demo
background
- 基于matlab，利用背景减除法实现运动目标的跟踪。本代码所用的函数均适用于新版本的matlab，不需要额外修改。在MATLAB2014a编译通过。-Based on matlab, for tracking moving objects using background subtraction method. The code used functions are applied to the new version of matlab, no additional modifications
frame_diff
- 基于matlab，利用帧间差分法实现运动目标的检测，本代码适用于新版本的matlab，不用使用者重新修改。在MATLAB2014a编译通过-Based on matlab, using inter-frame difference method to detect moving objects, this code applies a new version of matlab, the user need not re-edit. In MATLAB2014a compiler
BeliefPropagationStereo
- 用置信度传播算法计算视差图 里面含有比较完整的算法和相关资源-belief propagation stereo algorithm
SED2
- 超像素分割 毕业论文 matlab源代码-SDE SUPERPIXEL SEGMENT
face_test
- 基于vs2010+opencv2.4.4的人脸的检测，可以供各位初学的读者越来，以快速掌握人脸人检测，对于大神级人物，就不用读了。谢谢各位支持哦，祝各位学者，学习进步，事业有成。愿计算机视觉都能给你我带来美好的明天。-Face detect