hog silhouette

Hog feature +SVM is often used for pedestrian detection.OPENCV also has the Hog feature extraction of the original code, but because the original code is not written in Python, and skimage with Python implementation, so the interpretation of the Skimage code.First look at the Code for Hog feature extraction with the Skimage library: from Import

First, the basic hog algorithmHog feature was first appeared in the SIFT algorithm, because of its very strong image feature description ability, gradually known and widely used, and its target detection performance is particularly prominent.Hog Feature Extraction ProcessStep one: traverse the image each pixel point, take the 8*8 pixel domain as the grid (block) region as the center;Step two: Divide the grid (block) area evenly into 4 cell units of eq

 HOG, a gradient-oriented histogram (histograms of oriented Gradient), was first proposed by Navneet Dalal and Bill Triggs in 2005, initially for pedestrian detection. They extracted HOG features and used linear SVM classifier to detect pedestrians, and achieved good results. The HOG characteristic is put forward on the assumption that the edge information and

Directional gradient histogram (histogram of oriented Gradient, HOG) features advantages and disadvantages Description Advantage First of all, since the HOG is operated on the local cell of the image, it can maintain a good invariance to the geometric and optical deformation of the image. These two types of deformation will only appear in the larger space area. Secondly, in the rough airspace sampling, fin

Sketch posters are relatively concise, but need more silhouette material, can use ready-made shapes or hand over some of the desired elements into silhouette material.Final effect 1. First, we'll create a new file. Here we need to set the length, width and resolution. In our example, I intend to use A5 paper size, select "International Standard paper", then c

The basic understanding of hog is the reference to Dalal's histograms of oriented gradients for Human detection This paper, and reference to the on-line still image hog pedestrian detection code changed to the basic video of pedestrian detection.The basic idea of hog feature extraction:The appearance and shape of the local target can be well described by the dist

#include"iostream"#include"Queue"using namespacestd; #include"opencv2/opencv.hpp"#include"Windows.h"#include"opencv2/core/core.hpp"#include"opencv2/highgui/highgui.hpp"#include"opencv2/imgproc/imgproc.hpp"#include"opencv2/objdetect/objdetect.hpp"using namespaceCV;intMain () {Try{iplimage*pframe =NULL; Cvcapture*pcapture =NULL; //pcapture = cvcreatecameracapture (-1); //pcapture = Cvcapturefromcam (0);Pcapture = Cvcapturefromfile ("c:\\c_c++ Code\\photo and video\\textvideo2.flv"); //pcapture = C

This blog is only used for learning, if there are errors in the place, please correct me, if you need to reprint to indicate the source.See machine learning also has a period of time, these two days finally bravely stepped out the first step, realized the HOG+SVM on the picture classification, the specific code can be downloaded on GitHub, Https://github.com/subicWang/HOG-SVM-classifer. Everyone says

Hog is a gradient based histogram extraction algorithm, which is very effective for human detection. It has been implemented in the opencv2.2+ version. Encapsulated in the Hogdescriptor class. Hog is actually a picture of a designated size area for gradient statistics. can be called directly. OpenCV it too complicated, use the time to divide what Window,block,cell what ... A whole bunch of stuff. Here are t

", set reference to the following English settings, here to do so to stroke the outline of the previous pen Figure 4 Figure 5 5, copy we have done before the silhouette sketch contour Strokes 3 times, then use Gaussian blur for the topmost contour layer (guys blur copy), set the Blur range to 2, then select four layers at the same time, using the "multiply" in the blending mode, so that the previous whit

", cv_window_autosize); -Imshow ("Input Image", SRC); - - Mat DST, Dst_gray; +Resize (Src,dst,size ( -, -));//Change Size - + Cvtcolor (dst,dst_gray,color_bgr2gray); A atHogdescriptor Detector (Size ( -, -), Size ( -, -), Size (8,8), Size (8,8),9); -vectorfloat> descriptors;//histogram vector -Vectorlocations; -Detector.compute (Dst_gray, Descriptors,size (0,0), Size (0,0), locations); -printf"Number of HOG descriptors:%d", Descriptors.size ()); -

The direction gradient histogram (histogram of oriented Gradient, HOG) was proposed in 2005, is a commonly used feature extraction method, HOG+SVM in pedestrian detection has excellent results. The principle of hog feature extraction algorithm In an image, the direction density distribution of gradient or edge can well describe the characteristics of the local ta

! = Img_path.size (); i++) {src=cvloadimage (Img_path[i].c_str (),1); if(src = =NULL) {cout"can not load the image:"Endl; Continue; } cout"processing"Endl; Cvresize (SRC,TRAINIMG); //Reading PicturesHogdescriptor *hog=NewHogdescriptor (Cvsize ( -, -), Cvsize ( -, -), Cvsize (8,8), Cvsize (8,8),9);//See reference article for specific meaningsvectorfloat>descriptors;//result ArrayHog->compute (Trainimg, Descriptors,size (1,1), Size (0,0));//call the c

Pedestrian training:Http://www.tuicool.com/articles/MvYfuiCharacter Recognition: http://www.haogongju.net/art/2328003The approximate flow of training with OPENCV using hog features for SVM algorithm is 1) Set up the training sample setTwo sets of data are required, one is the category of the data, and the other is the vector information of the data.2) Set SVM parameters, refer to "machine mode->LIBSVM parameter description"Note that you must use linea

Online see about using OPENCV to classify the image, this time with Matlab to do some attempts, the image data set is: Link: https://pan.baidu.com/s/1i5OhC7z Password: utn7, other MATLAB version/HTTP blog.csdn.net/libin88211/article/details/19968205, click the Open link, http://blog.csdn.net/jcy1009015337/article/details/ 53763484 additional OPENCV versions for: Click to open the link, http://blog.csdn.net/always2015/article/details/47107129 Nonsense not to say, directly on the code (for MATLAB

how Photoshop makes silhouette visual effects Effect Chart: Material: Production Start: First the portrait is deducted, here with the channel buckle picture is the best, the figure quickly not so careful, will buckle the picture to put into the city image Create a new layer, fill the white, adjust the transparency, I tune the 80 Ctrl+t, put your face in the right place. To create a cli