Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time rendering
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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The sponsors for events such as motor sports can install billboard advertisements at event sites in return for investments. Checking how ads appear in a broadcast is important to confirm the effectiveness of investments and recognizing ads in videos is required to make the check automatic. This paper presents a method for recognizing multiple ads. After obtaining point correspondences between a model image and a scene image using local invariants features, we separate the point correspondences of an instance of an ad by calculating a homography using RANSAC. To make the use of RANSAC feasible, we develop two techniques. First, we use the ratio of distances of descriptors to reject outliers and introduce a novel scheme to set a threshold for the ratio of distances. Second, we incorporate an evaluation on appearances of ads into RANSAC to reject the homographies corresponding to appearances of ads which are never observed in actual scenes. The detail of a recognition algorithm based on these techniques is shown. We conclude with experiments that demonstrate recognition of multiple ads in videos.