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# Pragma warning (Disable: 4786) # include # include # include using namespace STD; int main () {string name [] = {"Tian", "DSP", "Su", "chu"}; Map mapname; For (INT I = 0; I (I, name [I]);} Map :: iterator mapit; For (mapit = mapname. begin ();

For image processing, the most time-consuming operation should be the convolution operation step. If you have the opportunity to use C ++ for image processing projects in the future, this convolution part should be optimized. MATLAB does not need to

In the past two days, I have helped a teacher modify the format of the paper. I have a deep understanding of the difficulties of using word to modify the paper. So I want to write my thesis or something in the future. If I do not get tired of making

For JPG compression principles, refer to the following article. You have not used the libjpeg compression code. Please refer to the following article. The libjpeg library compression is exactly the same as the decompression in the previous article.

The so-called box fuzzy here is actually the mean filter that you are familiar with before. The principle is to take the current pixel as the center, calculate the average of the two pixels around the radius R (2 * r + 1) and then assign the value

If it is not a grayscale image, the maximum value is meaningless. A color chart can also be converted into a grayscale image. Although assembly is used, multimedia commands are not used. The RGB values of gray images are the same. You do not need to

It is also pitted by domestic papers, and it is necessary to write papers with a conscience. Today I am looking at the content of local entropy. I think it is quite easy to read the content introduced in this paper. So I came up with the idea of

It is mainly used to learn multimedia commands, or I will not be so troublesome to use Win32 SDK programming. Sure enough, we only need to learn image algorithms, or we recommend Matlab. The pcmpgtb command is used here. Format: pcmpgtb mm0,

This has also been implemented in the past, but now I look back, it was really bad to write at that time, so now I decided to use MATLAB to rewrite it. And the method at that time is not very good. The method used here is in Feature Extraction and

As the name suggests, conditional expansion is a conditional expansion. There are usually two conditions. 1. expansion is always in the original set. For example: Original set image: Subset images inside the set: If the condition of rule 1 is

Mutual conversion between the color saturation brightness and the three primary colors Clear all; close all1_clc1_img1_imread('lena_color.jpg '); IMG = mat2gray (IMG); % ing any range to []; [m n dim] = size (IMG); imshow (IMG ); % image rgbr = IMG (

I wrote about the zooming of the nearest neighbor interpolation of MATLAB half a year ago, so I didn't think about the boundary issue. Previously, we used opencv to write bilinear interpolation to enlarge the image, but the writing was confusing. So

The so-called final corrosion does not mean that the image is continuously corroded until it is black. What else does that mean. Final corrosion means the union of all the remaining parts before the sub-areas disappear in the continuous corrosion

This is often used to calculate Haar features. Wiki has a very good introduction. I turned to the following shame: Each point of the integral graph (X,Y) Is the sum of all values in the upper-left corner of the source image: In addition, the

Automatic focus must judge the blur or clarity of the image. Of course, the real focus still needs to work with the hardware. Now I don't want to work with the hardware, so I don't care about that. There are three main evaluation methods: 1.

When each pixel in an image is regarded as a mass star, the gray value of the pixel is equivalent to the mass of the star. The law of gravitation is used to obtain the "force" of each pixel from other pixels and the force field image. The formula is

Here, the transformation formula is used to simulate the field. Although it is a digital image, it can be written in this way. Another formula is used to sample YCbCr. Clear all; close all1_clc1_img1_imread('lena_color.jpg '); IMG = mat2gray (IMG);

This YCbCr is derived from YUV and is suitable for processing digital images. JPEG compression is processed in this color space. Conversion formula. Clear all; close all1_clc1_img1_imread('lena_color.jpg '); % IMG = mat2gray (IMG); % ing any range

The Susan operator can detect both the corner points and the edge points, but the corner points seem to be inferior to Harris, and the edge seems to be inferior to those of Conway. However, the idea is a bit interesting. The main idea is to first

There are two types of stereo sensing Point Matching: one is low-level pixel-level matching, and the other is high-level feature-level matching. This section describes the underlying pixel-level matching. You can use the camera to move two images in