NDLI: Model-based 2D&3D dominant motion estimation for mosaicing and video representation

Content Provider	IEEE Xplore Digital Library
Author	Sawhney, H.S. Ayer, S. Gorkani, M.
Copyright Year	1995
Description	Author affiliation: Machine Vision Group, IBM Almaden Res. Center, San Jose, CA, USA (Sawhney, H.S.)
Abstract	It is fairly common in video sequences that a mostly fixed background (scene) is imaged with or without objects. The dominant background changes in the image plane mostly due to camera operations and motion (zoom, pan, tilt, track etc.). We address the problem of computation of the dominant image transformation over time and demonstrate how this can be effectively used for efficient video representation through video mosaicing and image registration. We formulate the problem of dominant component estimation as that of model based robust estimation using M estimators with direct, multi resolution methods. In addition to 2D affine and plane projective models, that have been used in the past for describing image motion using direct methods, we also employ a true 3D model of motion and scene structure imaged with uncalibrated cameras. This model parameterizes the image motion as that due to a planar component and a parallax component. For rigid 3D scenes imaged under camera motion only, least squares (LS) methods with the plane and parallax parameterization are also presented. Furthermore, in the context of robust estimation, in contrast with previous approaches for similar problems, our algorithm employs an automatic computation of a scale parameter that is crucial in rejecting the non dominant components as outliers.<>
Starting Page	583
Ending Page	590
File Size	902749
Page Count	8
File Format	PDF
ISBN	0818670428
DOI	10.1109/ICCV.1995.466886
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-06-20
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion estimation Layout Robustness Video sequences Tracking Cameras Computer vision Parametric statistics Machine vision Laboratories
Content Type	Text
Resource Type	Article

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