IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Field and Optical Flow: Qualitative Properties
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Creating full view panoramic image mosaics and environment maps
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
True Multi-Image Alignment and Its Application to Mosaicing and Lens Distortion Correction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Mosaic based representations of video sequences and their applications
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Note: Focal length and registration correction for building panorama from photographs
Computer Vision and Image Understanding
Semi-automatic registration of videos for improved watermark detection
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
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This paper studies the application of gradient-based motion detection techniques (i.e., optical flow methods) for registration of adjacent images taken using a hand-held camera for the purposes of building a panorama. A general 8-parameter model or a more compact 3-parameter model is commonly used for transformation estimation. However, both models are approximations to the real situation when viewpoint position is not absolutely fixed but includes a small translation, and thus distortion and blurring are sometimes present in the final registration results. This paper proposes a new 5-parameter model that shows better result and has less strict requirement on good choice of unknown initial parameters. An analysis of disparity recovery range and its enlargement using Gaussian filter is also given.