CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: STUDY OF SIFT DESCRIPTORS FOR IMAGE MATCHING BASED LOCALIZATION IN URBAN STREET VIEW CONTEXT David Picard, Matthieu Cord and Eduardo Valle

Document type:: Monograph

Structure type:: Chapter

Each keypoints of the database within the k-NN is added to the list of matches of the image it belongs. A basic method to retrieve images corresponding to the scene is to rank the images by descending order of the size of the lists of matches. 4 GEOMETRICAL CONSISTENCY Since every point of the query is associated with many points in the database, irrelevant points of the query will still influence the ranking. However, we can make the as sumption that for those matches, the relative positions of the query and target points within their respective images are not coherent.Thus, a geometric constraint over the en semble of matches between two images shall be able to remove the irrelevant matches. We test two criteria of geometrical consistency. The first criteria is to estimate the 2D affine transform between the two images, and then to remove the points not coherent with it. Although the transformation between the images is indeed 3D, we assume that under small perspective changes, a 2D affine transform is enough to catch the transforma tion of a single plane (in our case, the front of the build ing). The algorithm used to estimate the affine transform is RANSAC, a model estimation technique which can deal with a large fraction of outliers (Fischler and Bolles, 1981). An example of matches after the removal of non-coherent points is shown on Fig. 4. Figure 4: Matching points after the non-coherent to the estimate 2D affine transform matches have been remove. The second criterion is to keep only the matches which correspond to the most frequent angle difference between matched points (Jegou et al., 2008). This is done by com puting an histogram of the difference between the principal direction of the query and the target point of a match. We then keep the matches corresponding to the most frequent value in the histogram. A pairwise matching using a distance contrast crite rion (named Image Matching there after). A k-NN search plus a vote (named Brute vote). A k-NN search plus the angle differences consistency criterion (named Angle differences). A k-NN search plus the 2D affine transform consis tency criterion (name Ransac). We set k — 10 for the k-NN Search. The parameters for the RANSAC algorithm were empiricaly set to 15 pixels maximum distance to fit the model and minimum 3 inliers for the affine transformation. The first dataset consisted of 82 images of a single street (about 350 000 keypoints). The query set contained im ages taken by a mobile phone in front of some of the shops in the street. As the images (both in the query set and in the database) are direct views of the buildings, we considered this test as easy, since the transformation between query and its corresponding target images is simple. The second dataset contained 300 images of a large boulevard (about 3.5 millions of keypoints). The queries where taken with a mobile phone from the sidewalk. As the vehicle taking the pictures was in the middle of the street, the targeted re gions of the images (a shop, for instance) are very small. Thus, few keypoints of each image are describing some thing we might be looking for. As there are many severe transformations (scaling, viewpoint changes), we consider this test difficult. For both sets, we have manually built the groundtruth by annotating which images correspond to each query. We have used three criteria for the evaluation. The first consisted in measuring the rank of the first relevant image retrieved (average of the query set). The second measure was the evolution of the number of relevant image in the retrieved set, as the size of this set increased. The third criterion was the precision, the number of relevant images retrieved over the number of images retrieved. 5.2 Results on Dataset 1 An example of the first images retrieved using the Brute vote is shown on Fig. 5. The first images retrieved with this technique have about 2000 matching keypoints (im ages in this set contain about 5000 keypoints). There are several occlusions between the query image and the im ages of iTowns (for instance the car in front of the shop). However, a relevant image is found within the first images. Fig. 6 presents the same result, but with the Angle dif ferences refinement. The first images retrieved have about 200 matching keypoints. As we can see, the refinement introduced a re-ranking of the first images profitable to 195

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