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[matlab咖吗滤波matlab

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function [h,s,v] = rgb2hsv(r,g,b)
%RGB2HSV Convert red-green-blue colors to hue-saturation-value.
%   H = RGB2HSV(M) converts an RGB color map to an HSV color map.
%   Each map is a matrix with any number of rows, exactly three columns,
%   and elements in the interval 0 to 1.  The columns of the input matrix,
%   M, represent intensity of red, blue and green, respectively.  The
%   columns of the resulting output matrix, H, represent hue, saturation
%   and color value, respectively.
%
%   HSV = RGB2HSV(RGB) converts the RGB image RGB (3-D array) to the
%   equivalent HSV image HSV (3-D array).


Platform: | Size: 1517 | Author: leohee | Hits:

[Special EffectsImageEnhencementUsingIntensityTransformations

Description: 本实验要求使用强度变化方法对图像进行增强。图像增强的是要目标是处理图像,使其比原始图像更适用于特定应用,图像增强的方法分为两大类,空间域方法和频域方法,“空间域”一词是指图像平面本身,这类方法是以图像像素的直接处理。“频域”处理技术是以修改图像的傅里叶变换为基础的。本实验采用的对数变换和指数变换是对前一种方法的应用。-This experiment requires the use of intensity changes in methods of image enhancement. Image enhancement is to target is to deal with images, so that it more than the original image is more applicable to specific applications, image enhancement method is divided into two categories, spatial domain methods and frequency domain methods,
Platform: | Size: 808960 | Author: jhm | Hits:

[Special Effectsgrowcut

Description: 可实现与graph-cuts算法相似的图像分割效果. 他借用了生物形态学知识,将每一个像素视为一个细胞,这些细胞可能是前景,背景,或其他。这些细胞依据其灰度竞争获得生长,由此获得分割。-This algorithm is presented as an alternative to graph-cuts. The operation is very simple, and can be thought of with a biological metaphor: Imagine each image pixel is a "cell" of a certain type. These cells can be foreground, background, undefined, or others. As the algorithm proceeds, these cells compete to dominate the image domain. The ability of the cells to spread is related to the image pixel intensity
Platform: | Size: 94208 | Author: panghuanzhi | Hits:

[Special Effectsmatlab

Description: 算差分盒维数的matlab程序。 让窗口中的每个像素都对分数维作出贡献。首先,计算某一尺度窗口的平均灰度值 ,然后判断每一个像素的灰度 ,若大于灰度平均值 ,则累加其灰度值为 max ,若小于灰度平均值 ,则累加其灰度值为min ,用max 和min代替 在 Sarkar 和 Chaudhuri 算法中的最大值和最小值 ,再通过拟合求出分数维。 -Differential count box dimension matlab program. Let window on the fractal dimension of each pixel to contribute. First, the calculation of an average gray scale of the window, and then determine the gray scale of each pixel, if the intensity is greater than the average, the cumulative value of its gray max, if less than the average gray level, the accumulation of its gray value of min, max and min to use instead of the algorithm in Sarkar and Chaudhuri maximum and minimum, and then by fitting the calculated fractal dimension.
Platform: | Size: 1024 | Author: 冯家乐 | Hits:

[Special EffectsFingerprint-Enhancement

Description: 指纹增强的matlab实现源代码,包含多个matlab函数文件。-ridgesegment.m identifies ridge-like regions of a fingerprint image. It also normalises the intensity values of the image. ridgeorient.m estimates the local orientation of ridges in a fingerprint. plotridgeorient.m plots ridge orientations calculated by ridgeorient. ridgefreq.m estimates the local ridge frequency across a fingerprint image. freqest.m estimates the ridge frequency within a small block of an image. This is used by ridgefreq. ridgefilter.m enhances a fingerprint image using oriented filters.
Platform: | Size: 10240 | Author: tc | Hits:

[matlabFast-Tracking

Description: “Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码,效果非常好。-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 Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
Platform: | Size: 8955904 | Author: happy | Hits:

[Streaming Mpeg4FrequencyFilter

Description: It is a code that contains multiple functions used for image processing written in matlab language, like histogram spectrum, the median filter, a special image resize function , an intensity transform and a special image adjust in order to adjust all types of images.
Platform: | Size: 13312 | Author: AngelSark | Hits:

[matlab006-Histogram-Equalization---Matlab-Technique

Description: This histogram is a graph showing the number of pixels in an image at each different intensity value found in that image For an 8 bit grayscale image there are 256 different possible intensities , and so the histogram will graphically displays 256 numbers showing the distribution of pixels amongst these grayscale values
Platform: | Size: 161792 | Author: kokuon | Hits:

[matlabimnoise_bi

Description: 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 PLOT(IMAGE_INTENSITY,VAR) plots the functional relationship between noise variance and image intensity. IMAGE_INTENSITY must contain normalized intensity values ranging from 0 to 1.
Platform: | Size: 2048 | Author: Hoang Cuong | Hits:

[matlabdeimnoise2_bi

Description: 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 relationship between noise variance and image intensity. IMAGE_INTENSITY must contain normalized intensity values ranging from 0 to 1.
Platform: | Size: 1024 | Author: Hoang Cuong | Hits:

[OtherMatlab_STCv0

Description: 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 Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
Platform: | Size: 7226368 | Author: 大大大大大澈儿 | Hits:

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