We propose a content-based 3D mosaic (CB3M) representation for long video sequences of 3D and dynamic scenes captured by a camera on a mobile platform. The motion of the camera has a dominant direction of motion (as o...
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ISBN:
(纸本)0819462853
We propose a content-based 3D mosaic (CB3M) representation for long video sequences of 3D and dynamic scenes captured by a camera on a mobile platform. The motion of the camera has a dominant direction of motion (as on an airplane or ground vehicle), but 6 DOF motion is allowed. In the first step, a set of parallel-perspective (pushbroom) mosaics with varying viewing directions is generated to capture both the 3D and dynamic aspects of the scene under the camera coverage. In the second step, a segmentation-based stereo matching algorithm is applied to extract parametric representations of the color, structure and motion of the dynamic and/or 3D objects in urban scenes where a lot of planar surfaces exist. Multiple pairs of stereo mosaics are used for facilitating reliable stereo matching, occlusion handling, accurate 3D reconstruction and robust moving target detection. We use the fact that all the static objects obey the epipolar geometry of pushbroorn stereo, whereas an independent moving object either violates the epipolar geometry if the motion is not in the direction of sensor motion or exhibits unusual 3D structures. The CB3M is a highly compressed visual representation for a very long video sequence of a dynamic 3D scene. More importantly, the CB3M representation has object contents of both 3D and motion. Experimental results are given for the CB3M construction for both simulated and real video sequences to show the accuracy and effectiveness of the representation.
This paper describes a hybrid object-basedvideocoding scheme that achieves efficient compression by separating moving objects from stationary background and transmitting the shape, motion and residuals for each segm...
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ISBN:
(纸本)9781424403523
This paper describes a hybrid object-basedvideocoding scheme that achieves efficient compression by separating moving objects from stationary background and transmitting the shape, motion and residuals for each segmented object. In this scheme, a new content-based object segmentation algorithm is proposed, which does not assume any prior modeling of the objects being segmented. The binarization process, which finds large object regions, is based on a threshold function that calculates block histograms and takes image noise into account. The resultant binary mask is further processed using morphological operations. The motion vectors are estimated inside the change detection mask using block-matching method between two successive frames, and then the dense motion field is estimated using the motion vectors and the Horn-Schunck algorithm.
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