Paper
18 February 2009 Unsupervised object segmentation for 2D to 3D conversion
Author Affiliations +
Proceedings Volume 7237, Stereoscopic Displays and Applications XX; 72371B (2009) https://doi.org/10.1117/12.806237
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matthias Kunter, Sebastian Knorr, Andreas Krutz, and Thomas Sikora "Unsupervised object segmentation for 2D to 3D conversion", Proc. SPIE 7237, Stereoscopic Displays and Applications XX, 72371B (18 February 2009); https://doi.org/10.1117/12.806237
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CITATIONS
Cited by 13 scholarly publications and 4 patents.
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KEYWORDS
Image segmentation

Cameras

Image processing

Atomic force microscopy

Image analysis

3D image processing

Anisotropic filtering

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