In this paper, we address the handling of independently moving objects (IMOs) in automatic 2D to stereoscopic 3D
conversion systems based on structure-from-motion (SfM) techniques. Exploiting the different viewing positions of a
moving camera, these techniques yield excellent 3D results for static scene objects. However, the independent motion of
any foreground object requires a separate conversion process. We propose a novel segmentation approach that estimates
the occluded static background and segments the IMOs based on advanced change detection. The background estimation
is achieved applying 2D registration and blending techniques, representing an approximation of the underlying scene
geometry. The segmentation process itself uses anisotropic filtering applied on the difference image between original
frame and the estimated background frame. In order to render the segmented objects into the automatically generated 3D
scene properly, a small amount of user interaction will be necessary, e.g. an assignment of intra-object depth or the
object's absolute z-position. Experiments show that the segmentation method achieves accurate mask results for a
variety of scenes, similar to the masks obtained manually using state-of-the-art rotoscoping tools. Though, this work
contributes to the extension of SfM-based automatic 3D conversion methods for the application on dynamic scenes.
We present a robust and computational low complex method to estimate the physical camera parameters, intrinsic and extrinsic, for scene shots captured by cameras applying pan, tilt, rotation, and zoom. These parameters are then used to split a sequence of frames into several subsequences in an optimal way to generate multiple sprites. Hereby, optimal means a minimal usage of memory while keeping or even improving the reconstruction quality of the scene background. Since wide angles between two frames of a scene shot cause geometrical distortions using a perspective mapping it is necessary to part the shot into several subsequences. In our approach it is not mandatory that all frames of a subsequence are adjacent frames in the original scene. Furthermore the angle-based classification allows frame reordering and makes our approach very powerful.
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