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分解与合成
- 用Matlab中的Haar和Db9小波对图像进行分解和重构,并在不同阈值下用PNG格式存储重构的图像.-Matlab Haar and the right image Db9 wavelet decomposition and reconstruction, and under different thresholds with PNG format image storage remodeling.
OSTU
- 二维OSTU方法 ，能够自动给出合适阈值 matlab环境下开发-Two-dimensional OSTU method that can automatically give the appropriate thresholds developed under matlab environment
otsu
- OTSU Gray-level image segmentation using Otsu s method. Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection Method from Gray-Level Histograms, IEEE
Genetic
- 遗传算法优化神经网络的权值和阈值 matlab程序代码-Genetic algorithm optimization neural network weights and thresholds
robot
- 一个基于人工势场的机器人路径规划的仿真程序源码，可以自行修改阈值。-An artificial potential field based robot path planning simulation program source code, you can modify thresholds.
GAbp
- 遗传算法优化BP神经网络的权值和阈值，进行预测-BP neural network genetic algorithm to optimize the weights and thresholds to predict
mycanny.m
- canny算法的一个简易实现,关于两个阈值的连接问题采用了膨胀的方法,较为使用,经比较发现与matlab自带函数相差不大-canny algorithm is a simple implementation, two thresholds on the issue by the expansion of the connection method, the more use, by the comparison function that comes with little difference
the-BP-neural-network
- 遗传算法优化BP神经网络权值和阈值的通用MATLAB源码-Genetic algorithm to optimize the BP neural network weights and thresholds of common MATLAB source
利用遗传算法优化BP神经网络权值和阈值
- 利用遗传算法优化BP神经网络权值和阈值，内含程序和m文件(Using genetic algorithm to optimize BP neural network weights and thresholds, containing procedures and M files)
MOVING OBJECT DETECTION USING MATLAB
- 在基于前景检测的基于移动物体检测和车辆跟踪算法的实现中，针对广泛的应用来实现读取AVI文件，并将其分解为R，G和B组件。 执行各种操作并检测移动物体。 各个阶段的阈值决定了识别某些尺寸的移动物体的可能性。 移动物体也在其中跟踪。 MATLAB用于实现算法。 该算法用包含120帧的输入avi格式视频文件进行测试。 研究并实现了这些算法的各种应用。(In this foreground detection based moving object detection and vehicle track
PSO优化BP神经网络 MATLAB版本2016a
- PSO优化BP神经网络 MATLAB版本2016a PSO优化BP神经网络的权值和阈值 有详尽的注释 并结合2016a的新版本函数特性，优化了算法(PSO optimization BP neural network MATLAB version 2016a PSO optimization BP neural network weights and thresholds Detailed comments and combined with the 2016a new version of t
Archive
- This function extends the functionality of the built-in mat2gray by using quantiles instead of thresholds. This is especially useful when the data contains outliers.