sift away demo

Sift,the scale invariant Feature Transform, the invariant feature transform is an effective method to detect the uniqueness of images, to translate, rotate, scale and even affine transformations (such as taking pictures from different angles) to maintain invariant image local features. It can be easily applied to image matching applications, such as target detection and recognition, or the calculation of geometric transformations between images.The al

OpenCV image matching algorithm-sift, opencv image sift //utils.h#ifndef _UTILS_H#define _UTILS_H#include //utils.cpp#include "stdafx.h"#include "utils.h"#include // Sift. cpp # include "stdafx. h "# include Use INFO sift_info;sift(path1,path2,sift_info,true);showInfo(sift_info);

http://blog.csdn.net/stellar0/article/details/8741780Classification:Recently also pay attention to some image splicing articles, many many, especially the Panorama mosaic, in fact, similar to the Canon camera attached software, a lot of panoramic view mosaic, multi-image automatic software implementation splicing, composition (synthesis) a panoramic image (landscape).Sift algorithm, I knows, unable to carefully describe (just also posted 2 recent info

http://blog.csdn.net/zddblog/article/details/7521424Directory (?) [-] Scaling invariant feature transform matching algorithm scale invariant Feature transformsift Just Zdd zddmailgmailcom or zddhubgmailcom Sift Overview Gaussian Blur 12 Gaussian function Ivigos Blur of 2 images 3 Separating Gaussian Blur 1 Scale space theory Representation of 2 scale space Construction of 3 Gauss Pyramid

Sift is a very classical algorithm for image matching, but very complicated, want to own C or C + + implementation is very troublesome, if just use, there are some foreign high maintenance of SIFT library, the early stage as long as they can call, the key is to be familiar with the general flow, the SIFT library has an understanding, The exact work is done by sim

A detailed analysis of the matching algorithm of scale invariant feature transformScale invariant Feature Transform (SIFT)Just for FunZdd[email protected]or ([email protected])For starters, from David G.lowe's thesis to implementation, there are many gaps in this article to help you across.If you study Sifi to do the search, perhaps Opensse is more suitable for you, welcome to use.1. Sift OverviewScale inva

Transferred from: http://blog.csdn.net/zddmail/article/details/7521424 The original learning sift time, I think this article is worthy of detailed explanation of the two words, special turn. A detailed analysis of the matching algorithm of scale invariant feature transformScale invariant Feature Transform (SIFT)Just for Fun zdd zddmail@gmail.com For starters, from David G.lowe's thesis to implementation, t

Original URL: http://www.tuicool.com/articles/NbIJ73 http://blog.csdn.net/songzitea/article/details/16986423 Introduction This section is mainly about David Lowe's elaboration of the SIFT algorithm distinctive Image Features from Scale-invariant keypoints and Herbert Bay, Andreas Ess, Tinne tuyte Laars, Luc Van Gool, explains and summarizes the surf algorithm. Summary of SIFT feature extraction According t

These days continue to look at the Lowe of the Great God sift God, see the dizzy limbs cramp. Also drunk!!!! I really can't see it, let's have some dry goods. We know that OpenCV comes with a library of SIFT feature detection and match matching, which allows us to operate like fools. But the actual use of the time is not so simple. A typical OPENCV-based SIFT fea

A detailed explanation of scaling invariant feature transformation matching algorithmScale invariant Feature Transform (SIFT)Just for Fun zdd zddmail@gmail.com or (zddhub@gmail.com) For beginners, from David G.lowe's thesis to the realization, there are many gaps in this article to help you across. If you learn Sifi is to do search, perhaps opensse more suitable for you, welcome to use. 1. Summary of Sift

1, STFT (scale invariant Feature Transform) Introduction 1.1 Sift feature detection step 1.2 sift algorithm features 1.3 Sift algorithm can solve the problem 2, scaling space 2.1 Multi-resolution pyramid 2.2 Gauss Pyramid Build example 2.3 Gaussian scale space (using different parameters) 3, Dog spatial extremum detection (find key) 4, delete bad Extreme points (

The same as the usual, directly posted report ~Programe list:Programe was developed in the condition of Windows aswell as Linux server, programming language is Matlab (www.mathworks.c OM).classify.m, kmeans.m: function for K-means clustering.main_kmeans.m: main function for K-means clustering.hsvfeatureextraction.m: Extracting the HSV features for a picture.hsvfeaturesorting.m: Sorting the extracted HSV features.hsvmed.m,hsvmed_accuracy.m: Making the medclassifcation in HSV space and calculate i

Today is a coincidence to find the same outline of the nature of the article.If only we could find it earlier. However, "When you think it is too late, in fact, it is not too late" can also be consolation, but can not often confuse themselves, after all, I need to start running!Follow this outline to go down, perhaps there will be unexpected gains, and then put the problem of multi-perspective, perhaps it should be effective.Well, it's useless to want too much of anything else.I think the more i

David Lowe's sift has always been used by everyone. We can't compile it by ourselves. It's not as good as we can compile it. First, use sift to extract feature points from the target object as the basis for subsequent judgment. The purpose of the demo is to detect the target object in another video. We use opencv to read a video. Use

Original works, allow reprint, please be sure to use hyperlinks in the form of the original source of the article, author information and this statement. Otherwise, the legal liability will be investigated. http://underthehood.blog.51cto.com/2531780/658350by Raysaint 2011/09/051 reviewscombining paper [1] and Rob Hess's Open source sift code (found OpenCV2.3 's source is also used by Rob Hess's sift code) t

#-*-coding:utf-8-*-import cv2 import NumPy as NP from find_obj import Filter_matches,explore_match from Matplotlib im Port Pyplot as Plt def getsift (): ' Get and view sift feature ' img_path1 = '. /.. /data/home.jpg ' #读取图像 img = cv2.imread (img_path1) #转换为灰度图 gray= cv2.cvtcolor (img,cv2. Color_bgr2gray) #创建sift的类 sift = Cv2.