TY - GEN
T1 - Registration of 3D geometric model and color images using SIFT and range intensity images
AU - Inomata, Ryo
AU - Terabayashi, Kenji
AU - Umeda, Kazunori
AU - Godin, Guy
PY - 2011
Y1 - 2011
N2 - In this paper, we propose a new method for 3D-2D registration based on SIFT and a range intensity image, which is a kind of intensity image simultaneously acquired with a range image using an active range sensor. A linear equation for the registration parameters is formulated, which is combined with displacement estimations for extrinsic and intrinsic parameters and the distortion of a camera's lens. This equation is solved to match a range intensity image and a color image using SIFT. The range intensity and color images differ, and the pairs of matched feature points usually contain a number of false matches. To reduce false matches, a range intensity image is combined with the background image of a color image. Then, a range intensity image is corrected for extracting good candidates. Moreover, to remove false matches while keeping correct matches, soft matching, in which false matches are weakly removed, is used. First, false matches are removed by using scale information from SIFT. Secondly, matching reliability is defined from the Bhattacharyya distance of the pair of matched feature points. Then RANSAC is applied. In this stage, its threshold is kept high. In our approach, the accuracy of registration is advanced. The effectiveness of the proposed method is illustrated by experiments with real-world objects.
AB - In this paper, we propose a new method for 3D-2D registration based on SIFT and a range intensity image, which is a kind of intensity image simultaneously acquired with a range image using an active range sensor. A linear equation for the registration parameters is formulated, which is combined with displacement estimations for extrinsic and intrinsic parameters and the distortion of a camera's lens. This equation is solved to match a range intensity image and a color image using SIFT. The range intensity and color images differ, and the pairs of matched feature points usually contain a number of false matches. To reduce false matches, a range intensity image is combined with the background image of a color image. Then, a range intensity image is corrected for extracting good candidates. Moreover, to remove false matches while keeping correct matches, soft matching, in which false matches are weakly removed, is used. First, false matches are removed by using scale information from SIFT. Secondly, matching reliability is defined from the Bhattacharyya distance of the pair of matched feature points. Then RANSAC is applied. In this stage, its threshold is kept high. In our approach, the accuracy of registration is advanced. The effectiveness of the proposed method is illustrated by experiments with real-world objects.
UR - http://www.scopus.com/inward/record.url?scp=80053352651&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24028-7_30
DO - 10.1007/978-3-642-24028-7_30
M3 - 会議への寄与
AN - SCOPUS:80053352651
SN - 9783642240270
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 325
EP - 336
BT - Advances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings
T2 - 7th International Symposium on Visual Computing, ISVC 2011
Y2 - 26 September 2011 through 28 September 2011
ER -