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.>

The major inhibitors of rapid access to online video data are costs and management of capture and storage, lack of high-speed real-time delivery and non-availability of content and context based intelligent search and indexing techniques. The solutions for capture, storage and delivery maybe on the horizon, however the lack of visual content based indexing of video and image information may still inhibit as widespread a use of this information modality as that of text or tabular data is currently. We present techniques for compact visual representation of video data that will be useful for visual content based presentation and indexing. Video data comes in torrents-almost a megabyte every 30th of a second-but also affords the exploitation of relatively smoothly changing information over time. The techniques presented exploit the motion information across video frames to represent the underlying scene in a compact visual form as it is seen across many slowly varying frames in a video. Two classes of techniques are presented: (i) dominant motion estimation based techniques which exploit a fairly common occurrence in videos that a mostly fixed background (scene) is imaged with or without independently moving objects, and (ii) simultaneous multiple motion estimation and representation of motion video using layered representations.