Implementation of Gaussian filter

本文介绍了一种一维高斯滤波器的实现方法,并提供了详细的C++代码示例。该滤波器通过计算高斯系数来平滑数据,适用于信号处理等领域。

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代码片段,留作日后用。

 1 vector<double> GaussianFilter(const vector<double> data, double standard_deviation, int filterlength)
2 { //length of data
3
4 int length = data.size();
5
6 //position array of filter elements
7 int* position = new int[filterlength];
8 int middle = filterlength/2;
9 for(int i = 0; i <filterlength; i++)
10 {
11 position[i] = i - middle;
12 }
13 long double* gaussianCoeffiecients = new long double[filterlength];
14 long double temp1 = 1/sqrt(2* PI * square(standard_deviation));
15 long double temp2 = 2 * square(standard_deviation);
16 for(int i = 0; i <filterlength ; i++)
17 {
18 gaussianCoeffiecients[i] = temp1* exp(-square(position[i])/temp2);
19 }
20 //initilise result vector to be the same length
21 vector<double> result(length);
22
23 for (int i = 0 ; i < length; i++)
24 {
25 double temp = 0.0;
26 if(i < middle)
27 {
28 for(int j = middle - i ; j < filterlength; j++)
29 {
30 temp += data[i - middle + j] * gaussianCoeffiecients[j];
31 }
32 }
33 else if(i >= middle; i <= length - 1 - middle)
34 {
35 for(int j = 0; j < filterlength; j++)
36 {
37 temp += data[i-middle+j]* gaussianCoeffiecients[j];
38 }
39 }
40 else if (i > length - middle - 1)
41 {
42 int lastfilterelement = middle + difference(length,i)- 1;
43 for(int j = 0; j < lastfilterelement; j++)
44 {
45 temp += data[i-middle+j]* gaussianCoeffiecients[j];
46 }
47 }
48 else
49 {//Not Possiable
50 }
51 result[i] = temp;
52 }
53 //memory clean up
54 delete[] gaussianCoeffiecients;
55 delete[] position;
56
57 //return result array
58 return result;
59 }

  

转载于:https://www.cnblogs.com/JohnShao/archive/2011/08/24/2151463.html

### Difference Gaussian Rasterization Implementation and Issues In the context of computer graphics, difference Gaussian (DoG) rasterization plays a significant role in various applications such as edge detection, feature enhancement, and image processing tasks. The implementation involves convolving an input image with two different Gaussian kernels having distinct standard deviations σ1 and σ2 where typically σ2 > σ1. The mathematical formulation for DoG can be expressed as follows: \[ \text{DoG}(I(x,y)) = G(I(x,y); \sigma_1) - G(I(x,y); \sigma_2) \] where \( I(x,y) \) represents the intensity value at pixel location (x,y), while \( G(\cdot;\sigma_i) \) denotes convolution operation using Gaussian kernel parameterized by variance \( \sigma_i^2 \)[^1]. For efficient computation during rasterization process especially when dealing with large images or real-time rendering scenarios, optimizations are necessary. One approach is to leverage separable filters which reduce computational complexity from O(n²) down to linear time complexity O(2n). Another technique includes precomputing look-up tables containing values obtained after applying Gaussians over discrete intervals thus speeding up subsequent operations significantly [^2]. However, several challenges arise concerning accuracy versus performance trade-offs along with handling boundary conditions effectively without introducing artifacts into final rendered output. Additionally, selecting appropriate parameters like sigma ratios directly impacts results quality requiring careful tuning based on specific use cases under consideration. ```cpp // C++ code snippet demonstrating basic Difference of Gaussians filter application. void applyDifferenceOfGaussians(const cv::Mat& srcImg, cv::Mat& dstImg, double sigma1, double sigma2){ Mat blurredImage1; Mat blurredImage2; // Apply Gaussian Blur separately with given sigmas GaussianBlur(srcImg, blurredImage1, Size(), sigma1); GaussianBlur(srcImg, blurredImage2, Size(), sigma2); // Subtract second blurred image from first one subtract(blurredImage1, blurredImage2, dstImg); } ``` Issues encountered may also involve aliasing effects due to undersampling high-frequency components present within original scene geometry leading to visual distortions post-rasterization step unless adequately addressed through anti-aliasing techniques.
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