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数字图像处理的几个程序例子
- 这是几个数字图像处理的程序实例,有经典的傅立叶变换和反变换、图像增强(中值滤波、增强光照)还有染色体计数。它们都是经典算法的实现。-Here are several digital image processing examples including classical Fourier transform and its inverse, image enhancement (like median filtering, intensity enhancement) and chromosom
delphi灰度直方图
- 直方图是反映一幅图像中的灰度级与出现这种灰度的概率之间的关系图。-histogram is reflected image of a gray scale and intensity of this relationship between the probability map.
PixelProfile
- 一個可擷取出 Image 中的 Pixel Slice 並顯示出 Pixel Value/Linear Distance 的直方圖, 包含 Red, Green, Blue, Intensity, Hue, Saturation 等資訊顯示, 並有資料分析.-can have just one of Pixel Image Slice showed Pixel Value / Linear Distance histogram contains Red, Green, Blue, Intensi
ThresholdTrans55556231
- 该函数用来对图像进行阈值变换。对于灰度值小于阈值的象素直接设置 灰度值为0;灰度值大于阈值的象素直接设置为2-the function used to image transform threshold. For gray value less than the threshold value of the pixel intensity value directly set to 0; Gray value greater than the threshold value of the p
imageimpose
- 数字图像矩阵数据的显示及其傅立叶变换 二维离散余弦变换的图像压缩 采用灰度变换的方法增强图像的对比度 直方图均匀化 模拟图像受高斯白噪声和椒盐噪声的影响 采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波 用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波 图像的自适应魏纳滤波 运用5种不同的梯度增强法进行图像锐化 图像的高通滤波和掩模处理 用巴特沃斯(Butterwo
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- 矩阵中的每一个元素称为像元、像素或图像元素。而g(i, j)代表(i, j)点的灰度值,即亮度值。 由于g (i, j)代表该点图像的光强度(亮度),而光是能量的一种形式,故g (i, j)必须大于零,且为有限值,即: 0<=g (i, j)<2n。 用g (i, j)的数值来表示(i, j)位置点上灰度级值的大小,即只反映了黑白灰度的关系。 数字化采样一般是按正方形点阵取样的, -each of the matrix elements known as a pixel, pixe
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- 矩阵中的每一个元素称为像元、像素或图像元素。而g(i, j)代表(i, j)点的灰度值,即亮度值。 由于g (i, j)代表该点图像的光强度(亮度),而光是能量的一种形式,故g (i, j)必须大于零,且为有限值,即: 0<=g (i, j)<2n。 用g (i, j)的数值来表示(i, j)位置点上灰度级值的大小,即只反映了黑白灰度的关系。 数字化采样一般是按正方形点阵取样的, -each of the matrix elements known as a pixel, pixe
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- 矩阵中的每一个元素称为像元、像素或图像元素。而g(i, j)代表(i, j)点的灰度值,即亮度值。 由于g (i, j)代表该点图像的光强度(亮度),而光是能量的一种形式,故g (i, j)必须大于零,且为有限值,即: 0<=g (i, j)<2n。 用g (i, j)的数值来表示(i, j)位置点上灰度级值的大小,即只反映了黑白灰度的关系。 数字化采样一般是按正方形点阵取样的, -each of the matrix elements known as a pixel, pixe
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- 矩阵中的每一个元素称为像元、像素或图像元素。而g(i, j)代表(i, j)点的灰度值,即亮度值。 由于g (i, j)代表该点图像的光强度(亮度),而光是能量的一种形式,故g (i, j)必须大于零,且为有限值,即: 0<=g (i, j)<2n。 用g (i, j)的数值来表示(i, j)位置点上灰度级值的大小,即只反映了黑白灰度的关系。 数字化采样一般是按正方形点阵取样的, -each of the matrix elements known as a pixel, pixe
Kmeans.Cluster.using.Guide
- 图像集群(Image Clustering) (1)图像读入,显示图像所在路径; (2)采用imgcluster函数进行图像集群,选择集群个数后进行图像集群; (3)运行后,在原图像上显示集群灰度图; (4)若要显示各个集群情况,可打开【Show Clustering Image】新窗体,显示各集群类的基于原图的彩绘区域。其中非当前集群范围,则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) re
Tper prentod
- This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD) wi
imageEnhancement
- 用于图像增强,在亮度和去噪方面,更适用于学生实验,验证等(this file contains two parts: the original images and the codes. this code is for image enhancement. To enhance images by improving the intensity and denoising)
imnoise_bi
- J = imnoise(I,'localvar',IMAGE_INTENSITY,VAR) adds zero-mean, Gaussian noise to an image, I, where the local variance of the noise is a function of the image intensity values in I. IMAGE_INTENSITY and VAR are vectors of the same size, and P
deimnoise2_bi
- adds zero-mean, Gaussian noise to an image, I, where the local variance of the noise is a function of the image intensity values in I. IMAGE_INTENSITY and VAR are vectors of the same size, and PLOT(IMAGE_INTENSITY,VAR) plots the functional
图像的分析
- 可显示图像中指定点的坐标值、像素值、图像强度曲线、图像像素强度曲线、等值线、均值、标准差、协方差系数、质心、边界等。(It can display coordinate values, pixel values, image intensity curves, pixel intensity curves, contour lines, mean value, standard deviation, covariance coefficient, centroid, boundary and s
onaatiohan
- By using the local regional information which has the ability to enhance the image, an improved active contour model based on level set method is proposed. Defining a novel SPF function with a nonnegative kernel function and local intensity cluste
ctivrougr
- A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale var
Matlab_STCv0
- In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a
cvpr16_deblur_study-master
- 文献 "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior" 的参考代码 用Lp正则化方法做盲复原的代码 demo_text_deblurring 是主函数(refer to "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior" main function: demo_t
text_deblurring_code
- Matlab code for "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior"; demo_text_deblurring 是主函数(refer to Deblurring Text Images via L0-Regularized Intensity and Gradient Prior)