搜索资源列表
Image-Gray-transform
- 实现图像灰度变换的功能，包括灰度线性变换、分段线性变换、灰度直方图、灰度分布均衡化等变换。-Image Gradation transformation, including gray linear transformation, piecewise linear transformation, histogram, gray distribution equalization transform, and so on.
DoG
- 高斯模糊是一种图像滤波器，它使用正态分布(高斯函数)计算模糊模板，并使用该模板与原图像做卷积运算，达到模糊图像的目的-Gaussian blur filter is a kind of image, it uses the normal distribution (gaussian function) to calculate fuzzy template, and use the template and the original image for convolution operation
ideo101setup
- ideo101setup.zip - This is the latest distribution of ideo. It contains 20 lessons on the basic 92 ideograms (hiragana and katakana). Remove any ideo installation before running this setup.-ideo101setup.zip- This is the latest distribution of ideo. I
ideo101
- ideo101.zip - This is the latest distribution of ideo. It contains 20 lessons on the basic 92 ideograms (hiragana and katakana). If you download this archive, your system must have the VB6 runtime already installed. To run the program, simply unzip
create_mix_gaussian
- 创建一个混合的高斯正太分布图，先初始化，再创建，最后出图-Create a mixture of Gaussian normal distribution chart, the first initialized, create, and finally the plot
create_mix_2D_gaussian
- 创建一个二维混合的高斯正太分布图，先初始化，再创建，最后出图-Create a two-dimensional Gaussian mixture normal distribution chart, the first initialized, create, and finally the plot
plot_mix_gaussian
- 绘制取样点的估计，对于一维图像，绘制正太分布，对于二维图像，绘制取样的轮廓图。-Draw sample point estimating, for the one-dimensional images, draw a normal distribution, for two-dimensional images, draw a sample contour.
plot_normal
- 已知参数mu和sigma，来绘制正太高斯分布-Known parameters Mu and Sigma, to draw a Gaussian distribution
fit_mix_gauss
- 对参数使用最大期望算法进行拟合成混合型高斯分布。-fit parameters for a mixed-gaussian distribution using EM algorithm
histeqCompute
- 直方图均衡化是将原图像通过某种变换，得到一幅灰度直方图为均匀分布的新图像的方法。-Histogram equalization is a new method to obtain a uniform distribution of the original image through a certain transformation.
senior
- 基于最小生成树的图像分割，并分配深度。最小生成树算法可以选择避圈法和破圈法，深度分配采用上远下近的假设。-Image segmentation based on minimum spanning tree, and assign depth. Minimum spanning tree algorithm may choose to avoid the law and break the circle circle method, depth distribution using a far l
spur_depth
- 高阶统计量和小波变换分别求模糊聚焦前景。并按高度深度线索分配深度。-Higher-order statistics and wavelet transform fuzzy focus were seeking prospects. Press distribution depth height depth cues.
gaosi
- 高斯混合模型算法实现c++，高斯混合模型就是用高斯概率密度函数（正态分布曲线）精确地量化事物，它是一个将事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型-Gaussian mixture model algorithm c++, Gaussian mixture model is a Gaussian probability density function (normal distribution curve) to accurately quantify things, it
watersheld
- 自己总结的分水岭算法，文章是对分水岭算法的分布详细介绍和实例，希望能帮到大家-Summing up their own watershed algorithm, the article is a detailed descr iption of the distribution of watershed algorithm and examples, I hope to help everyone
ZhifangtuJunheng
- 该程序为直方图均衡化程序，附有GUI界面，操作简单，可现实处理前后图像对比及处理前后的直方图分布情况显示，使用方便。-The program for histogram equalization program, with GUI interface, simple operation, the histogram distribution around the reality that is processing the image before and after treatment, ea
LDLPackage
- 该代码主要实现标签分布学习算法（Label Distribution Learning，LDL）对机器学习中多标签分类问题的改进和提升-The code mainly achieves label distribution learning algorithm(Label Distribution Learning, LDL) for machine learning multi-label classification improvement and upgrading
StdDistr
- 求标准正态分布，可精确到任意指定小数位。-For standard normal distribution, can be accurate to any specified decimal places.
GaussianMixture
- 高斯混合模型就是用高斯概率密度函数（正态分布曲线）精确地量化事物，它是一个将事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型。-Gaussian mixture model is to use the gaussian probability density function (gaussian distribution curve) accurately quantify things, it is a to break things down for several based
LevelSet
- level set水平集图像分割算法matlab程序实现-This Matlab/C code contains routines to perform level set image segmen- tation according to: (1): various multiphase (multiregion) formulations, including a fast scheme where the computation load grows linearly with
Get-Pixels
- 在给定的ROI范围内检测图像的像素分布，用于下一步图形分析-Detects the pixel distribution of the image in a given ROI range, and is used to analyze the image in the next step