搜索资源列表
DCT
- 实现对一幅灰度和彩色图像作的离散余弦变换,选择适当的DCT系数阈值对其进行DCT反变换.-The realization of a piece of gray and color images for the discrete cosine transform, select the appropriate thresholds for DCT coefficients of its inverse transform DCT.
GABPMATLAB
- 利用遗传算法优化BP神经网络权值和阈值，然后进行训练，利用训练好的模型进行预估。附实例-The use of genetic algorithm to optimize BP neural network weights and thresholds, and then training, training a good use of forecast models. Attached examples
thresholds
- 两种阈值基于小波变换的阈值方法的比较和介绍，并给出了详细的说明-Two thresholds based on wavelet transform thresholding methods and the introduction and give a detailed descr iption of
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
chap4
- 程序代码说明 P0401：用Prewitt算子检测图像的边缘 P0402：用不同σ值的LoG算子检测图像的边缘 P0403：用Canny算子检测图像的边缘 P0404：图像的阈值分割 P0405：用水线阈值法分割图像 P0406：对矩阵进行四叉树分解 P0407：将图像分为文字和非文字的两个类别 P0408：形态学梯度检测二值图像的边缘 P0409：形态学实例——从PCB图像中删除所有电流线，仅保留芯片对象
ObjectTrackingAndOptimalThresholding
- The application works with non-compressed grayscale AVI files. Run mainGUI.p. Load AVI file first using "Load". To process the next frame use "Step". To run the application until stop, use "Run" and "Stop". You
compared
- 对比BP，OMP，StOMP对自然图像的重构效果,输入图像是一幅256*256的lena图像，我们使用压缩采样后，再用上述三种方法对其进行重构，比较其结果，其中StOMP方法是用不同的阈值-Contrast to BP, OMP, StOMP effect on the reconstruction of natural images, input image is a 256* 256 lena image, we use the compressed sampled, and then re
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.
rocplot
- ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high spe
sw
- 1。访问在任何需要的帧所需像素 2。执行像素逐相对于第一帧帧差分像素 3。申请分类阈值 4。执行像素按有关透水框架像素帧差分 5。结合阈值的像素结果由像素帧差分-1. Access any desired pixel in any desired frame 2. Perform pixel-by-pixel frame differencing with respect to the first frame 3. Apply classification thresho
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
quyushengzhangfa
- 用matlab编写的区域生长法的程序，通过选择种子和阈值，可以进行图像分割和检查瑕疵-Written by matlab region growing process, by selecting the seeds and thresholds for image segmentation and inspection for defects
MATLAB
- MATLAB中实现了信号的阈值去噪，主要包括阈值去噪和阈值获取两方面。-MATLAB to achieve a threshold value of the signal de-noising, including thresholding and get two thresholds.
MATLAB
- 图像的直线提取，利用了hough变换，可以修改阀值选择需要的直线长度-Line extraction of the image using the hough transform, you can modify the thresholds to the desired length of the line
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
segbatch
- 使用植被指数ExG-ExR和固定阈值以及形态学方法分割绿色植被区域(Green vegetation areas were segmented using vegetation indices ExG-ExR and fixed thresholds as well as morphological methods)
1
- 利用基于直方图的自适应阈值方法实现分割前景与背景a）设定三个不同阈值，直接观察分割结果； b) 利用统计直方图，得到一个自适应的阈值.(The segmentation foreground and background a are realized by using the histogram based adaptive threshold method, and three different thresholds are set, and the segmentation
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.