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Panoramic Imaging using SURF and SIFT of
Eric Jansen – 2210105030
Computer Engineering and Telematics
Department of...
figure 1. img03.jpg
figure 2. img04.jpg
figure 3. SIFT-method keypoints-img01.jpg
figure 4. SIFT-method keypoints-img02.jpg
std::vector< std::vector<cv::DMatch> >
As shown in figure 3 and 4, the ...
const_iterator itM=
for (; itIn!=inliers.end();
++itIn,++itM) ...
figure 5. SIFT-method matching Key Points
between two images
3 Conclusion
With several experiments in merging these im-
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Panoramic Imaging using SIFT and SURF


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Panoramic Imaging using SIFT and SURF

  1. 1. Panoramic Imaging using SURF and SIFT of OpenCV Eric Jansen – 2210105030 Computer Engineering and Telematics Department of Electrical Engineering Institut Teknologi Sepuluh Nopember November 5th , 2011 Abstract Technology to provide good panoramic images or mosaic images are existed several years ago. And one of the hot topics being developed till today. In this experiment, several images are combined with finding their matching keypoints to provide image stitching. To solve this prob- lem, the concept of scale-invariant features has been introduced in computer vision. The main idea here is to have a scale factor associated with each of the detected feature points. In recent year, several scale-invariant features have been proposed and this recipe presents two of them, the SURF and SIFT features. SURF stands for Speeded Up Robust Features, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. The SIFT or Scale Invariant Fea- ture Transform is developed from the SURF al- gorithm as an efficient variant of another well- known scale-invariant feature detector. SIFT also detects features as local maxima in image and scale space, but uses the Laplacian filter response instead of the Hessian determinant. This Lapla- cian at different scales is computed using differ- ence of Gaussian filters. OpenCV has a wrapper class that detects these features and it is called in a similar way to the SURF features. 1 Preface Methods in determining the matching points be- tween two images are determined in calculating the color intensity of the images. Previous to determining the intensity. The images are then converted into grayscale color, as to combine three-scale of RGB colors into one scale (black to white or from 0 to 255). After converting im- ages into grayscale-color, with Hessian method of SURF, the images are normalized for better re- sults. OpenCV provides all these features, even the matching key points between images that are going to be warped. with RANSAC or Random Sample Consensus, the key points are directly searching the detected matches points at other images. 2 Methodology The algorithms of both SURF and SIFT are de- scribed as the followings: 2.1 Detecting the scale-invariant SURF and SIFT features The OpenCV implementation of SURF fea- tures also use the cv::SurfFeatureDetector or cv::SiftFeatureDetector of SIFT features in- terface. std::vector<cv::KeyPoint> keypoints; // Construct the SURF or SIFT features // detector object cv::Ptr<cv::FeatureDetector> feature = new cv::SurfFeatureDetector(2500); // or new cv::SiftFeatureDetector; // or the SIFT feature detector object // Detect the SURF features or // detect the SIFT features feature->detect(image,keypoints); Using cv::drawKeypoints OpenCV function to show the scale factor associated with each fea- ture: // Draw the keypoints with scale cv::drawKeypoints(image,keypoints, featureImage, cv::Scalar(255,255,255), cv::DrawMatchesFlags:: DRAW_RICH_KEYPOINTS); 1
  2. 2. figure 1. img03.jpg figure 2. img04.jpg figure 3. SIFT-method keypoints-img01.jpg figure 4. SIFT-method keypoints-img02.jpg 2.2 Feature Matching In both images, a SURF or SIFT feature has been detected at that location and the two corre- sponding circles (of different sizes) contain in the same visual elements. Based on the std::vector of cv::KeyPoint instances obtained from feature detection, the descriptors are obtained as follows: cv::Ptr<cv::DescriptorExtractor> m_pExtractor = new cv:: SurfDescriptorExtractor; // or cv::Ptr<cv::DescriptorExtractor> m_pExtractor = new cv:: SiftDescriptorExtractor; m_pExtractor->compute(image1,keypoints1, desc1); m_pExtractor->compute(image2,keypoints2, desc2); The result is a matrix which will contain as many rows as the number of elements in the keypoints vector. Each of these rows is an N-dimensional descriptor vector. In the case of the SURF de- scriptor, by default, it has a size of 64. This vec- tor characterizes the intensity pattern surround- ing a feature point. The more similar the two feature points, the closer their descriptor vectors should be. These descriptors are particularly useful in im- age matching. Suppose, for example, that two images of the same scene are to be matched. This can be accomplished by first detecting features on each image, and then extracting the descriptors of these features. Each feature descriptor vector in the first image is then compared to all feature descriptors in the second image. The pair that obtains the best score is then kept as the best match for that feature. This process is repeated for all features in the first image. This is the most basic scheme that has been implemented in OpenCV as the cv::BruteForceMatcher. It us used as follows: cv::BruteForceMatcher< cv::L2<float> > matcher; From image 1 to image 2 and image 2 to image 1 based on k-nearest neighbours with k=2. This is accomplished by the cv::BruteForceMatcher::knnMatch method as shown as follows: std::vector< std::vector<cv::DMatch> > matches1; matcher.knnMatch(desc1,desc2,matches1,2); 2
  3. 3. std::vector< std::vector<cv::DMatch> > matches2; matcher.knnMatch(desc2,desc1,matches2,2); As shown in figure 3 and 4, the white lines link- ing the matched points show that even if there are a large number of good matches, a significant number of false matches have survived. There- fore, the second test will be performed to filter more false matches. The following method called symmetrical matching scheme imposing that, for a match pair to be accepted, both points must be the best matching feature of the other: for (std::vector< std:: vector<cv::DMatch> >:: const_iterator matIt1= matches1.begin(); matIt1!=matches1.end(); ++matIt1) { if (matIt1->size() < 2) continue; for (std::vector< std::vector <cv::DMatch> >:: const_iterator matIt2= matches2.begin(); matIt2!=matches2.end(); ++matIt2) { if (matIt2->size() < 2) continue; if (*matIt1)[0].queryIdx == (*matIt2)[0].trainIdx && (*matIt2)[0].queryIdx == (*matIt1)[0].trainIdx) { symMatches.push_back( cv::DMatch( (*matIt1)[0].queryIdx, (*matIt1)[0].trainIdx, (*matIt1)[0].distance)); break; } } } 2.3 Outlier Rejection using RANSAC This test is based on the RANSAC method that can compute the fundamental matrix even when outliers are still present in the match set. The RANSAC algorithm aims at estimating a given mathematical entity from a data set that may contain a number of outliers. The idea is to randomly select some data points from the set and perform the estimation only with these. The number of selected points should be the minimum number of points required to estimate the math- ematical entity. The central idea behind the RANSAC algo- rithm is that the larger the support set is, the higher the probability that the computed matrix is the right one. Obviously, if one or more of the randomly selected matches is a wrong match, then the computed fundamental matrix will also be wrong, and its support set is expected to be small. This process is repeated a number of times, and at the end, the matrix with the largest support will be retained as the most probable one. When using the cv::findFundamentalMat func- tion with RANSAC, two extra parameters are provided. The first one is the confidence level that determines the number or iterations to be made. The second one is the maximum distance to the epipolar line for a point to be consid- ered as an inlier. All matched pairs in which a point is at a distance from its epipolar line larger than the one specified will be reported as an outlier. Therefore, the function also returns a std::vector of char value indicating that the corresponding match has been identified as an outlier (0) or as an inlier (1). for (std::vector< std:: vector<cv::DMatch> >:: const_iterator it= matches.begin(); it!=matches.end(); ++it) { float x = keypoints1[it-> queryIdx].pt.x; float y = keypoints2[it-> queryIdx].pt.y; points1.push_back(cv:: Point2f(x,y)); x = keypoints2[it-> trainIdx].pt.x; y = keypoints2[it-> trainIdx].pt.y; points2.push_back(cv:: Point2f(x,y)); } std::vector<uchar> inlier( points1.size(),0); fundamental = cv:: findFundamentalMat( cv::Mat(points1), cv::Mat(points2), inliers, CV_FM_RANSAC, m_lfDistance, m_lfConfidence); std::vector<uchar>:: const_iterator itIn= 3
  4. 4. inliers.begin(); std::vector<cv::DMatch>:: const_iterator itM= matches.begin(); for (; itIn!=inliers.end(); ++itIn,++itM) { if (*itIn) outMatches. push_back(*itM); } The more good matches found in initial match set, the higher the probability that RANSAC will give the correct fundamental matrix. This is why applying several filters to the match set before calling the cv::findFundamentalMat function. 2.4 Recovery Homography from Correspondences After computing the fundamental matrix of an image pair from a set of matches. Another math- ematical entity exists that can be computed from match pairs: a homography. Like the fundamen- tal matrix, the homography is a 3×3 matrix with special properties and, it applies to two-view im- ages in specific situations. The projective rela- tion between a 3D point and its image on a cam- era is expressed by a 3×4 matrix. The special case where two views of a scene are separated by a pure rotation, then it can be observed that the fourth column of the extrinsic matrix will be mad of all null. As a result, the projective rela- tion in this special case becomes a 3×3 matrix. This matrix is called a homography and it implies that, under special circumstances, e.g. rotation, the image of a point in one view is related to the image of the same point in another by a linear relation:   sx sy s   = H   x y 1   In homogenous coordinates, this relation holds up to a scale factor represented here by the scalar value s. Once this matrix is estimated, all points in one view can be transferred to the second view using this relation. Note that as a side effect of the homography relation for pure rotation, the fundamental matrix becomes undefined in this case. Suppose two images separated by a pure ro- tation. These two images can be matched using RobustMatcher class, and applying a RANSAC step which will involve the estimation of a ho- mography based on a match set that contains a good number of outliers. This is done by using the cv::findHomography function: std::vector<uchar> inliers( points1.size(),0); cv::Mat homography = cv::findHomography(cv:: Mat(points1),cv:: Mat(points2),inliers, CV_RANSAC,1); The resulting inliers that comply with the found homography have been drawn on those images by the following loop: std::vector<cv::Point2f>:: const_iterator itPts= points1.begin(); std::vector<uchar>:: const_iterator itIn= inliers.begin(); while (itPts != points1.end()) { if (*itIn) cv::circle(image1,*itPts, 3, cv::Scalar( 255, 255, 255),2); ++itPts; ++itIn; } itPts = points2.begin(); itIn = inliers.begin(); while (itPts != points2.end()) { if (*itIn) cv::circle(image2, *itPts, 3, cv::Scalar( 255, 255, 255),2); ++itPts; ++itIn; } 2.5 Image Compositing Once the homography is computed, it will trans- fer image points from one image to the other. In fact, for all pixels of an image and the result will be to transform this image to the other view. There is an OpenCV function that does exactly this: cv::warpPerspective(image1,result, homography,cv::Size(2 * image1.cols,image1.rows)); cv::Mat half(result,cv::Rect( 0,0,image2.cols,image2.rows)); image2.copyTo(half); 4
  5. 5. figure 5. SIFT-method matching Key Points between two images 3 Conclusion With several experiments in merging these im- ages, the SIFT algorithm compatibilities are higher than the SURF. Although in fact, the panoramic image produced of the SURF algo- rithm is the fastest. The SURF’s Hessian method for matching key points were set to 1000 could be applied to combine several images but with less accurate matching points. figure 6. Panoramic Image 5