资源列表
CImg
- CImg 库是一个免费、开源的图像处理C++库,名称原意是 Cool Image,正如其名,CImg是一个非常优秀、功能强大、代码简洁、使用方便的C++ 图像处理库-The CImg Library is a small, open-source, and modern C++ toolkit for image processing, designed with these properties in mind
MRMRF
- 基于MRF图形的小波与分解 基于MRF图形的小波与分解 基于MRF图形的小波与分解-Wavelet and decomposition based on MRF graphWavelet and decomposition based on MRF graphWavelet and decomposition based on MRF graphWavelet and decomposition based on MRF graph
grabcut.py
- opencv-python，交互式grabcut实现图像前景的提取，好用，效果好。-Opencv-python, interactive grabcut image foreground extraction, easy to use, can get good results.
adptive-cannny
- 结合canny算子的自适应阈值分割和边缘提取算法，matlab-Combined with canny operator prepared adaptive threshold segmentation edge extraction algorithm, matlab
VM
- 维纳滤波 维纳滤波 维纳滤波-wiener filtering
releaseV301
- edgeBox源码，通过边缘来检测图像中的物体，输出多个proposal。32位系统可用，64位系统需自行生成mex文件。-EdgeBox source code, through the edge to detect images of objects, the output of a number of proposals. 32-bit system is available, 64-bit system to generate mex file.
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