Panoramic viewfinder: providing a real-time preview to help users avoid flaws in panoramic pictures
OZCHI '05 Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Discovering panoramas in web videos
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Technical Section: All around the map: Online spherical panorama construction
Computers and Graphics
Detection and compression of moving objects based on new panoramic image modeling
Image and Vision Computing
Exploiting distinctive visual landmark maps in pan-tilt-zoom camera networks
Computer Vision and Image Understanding
Key frames selection into panoramic mosaics
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Efficiently locating photographs in many panoramas
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient representation of zooming information in videos using multi resolution mosaics
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
GPU-accelerated hierarchical dense correspondence for real-time aerial video processing
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Sprite generation using sprite fusion
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Panoinserts: mobile spatial teleconferencing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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We present an automatic and efficient method to register and stitch thousands of video frames into a large panoramic mosaic. Our method preserves the robustness and accuracy of image stitchers that match all pairs of images while utilizing the ordering information provided by video. We reduce the cost of searching for matches between video frames by adaptively identifying key frames based on the amount of image-to-image overlap. Key frames are matched to all other key frames, but intermediate video frames are only matched to temporally neighboring key frames and intermediate frames. Image orientations can be estimated from this sparse set of matches in time quadratic to cubic in the number of key frames but only linear in the number of intermediate frames. Additionally, the matches between pairs of images are compressed by replacing measurements within small windows in the image with a single representative measurement. We show that this approach substantially reduces the time required to estimate the image orientations with minimal loss of accuracy. Finally, we demonstrate both the efficiency and quality of our results by registering several long video sequences.