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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
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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. Ima
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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 ima
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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
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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.
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“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-te
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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
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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 graphic
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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
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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
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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
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