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Motion vectors and converted result for a zoom image: (a) direction of motion vectors; (b) interlaced stereoscopic converted result
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In this paper, we propose an algorithm that creates three-dimensional (3D) stereoscopic video from two-dimensional (2D) video encoded by H.264 instead of using two cameras conventionally. Very accurate motion vectors are available in H.264 bit streams because of the availability of a variety of block sizes. 2D/3D conversion algorithm proposed in th...
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... 3D stereoscopic images are displayed properly. To verify the quality of converted 3D images, a 3D stereoscopic monitor with a pair of polarized glasses was used. Fig. 10 shows an improved converted image with cut detection process when a rapid scene change occurs. During a scene change, if cut detection is not available, both the current and totally different delayed images are overlapped as shown in Fig. 10(a), resulting in no 3D stereoscopic effect. In contrast, Fig. 10(b) shows much better result when cut detection is enabled. Fig. 11 shows the stereoscopic conversion of a static image. Fig. 11(a) shows motion vectors of a given image. As evident in this figure, motion vectors are negligible; therefore the image is categorized as a static image. Fig 11(b) shows the result after the stereoscopic conversion. The red box in the figure indicates a reconstructed image shape after conversion process has been applied. The stereoscopic image shows more disparity in the corners than at the center of the image. In Fig. 12, the stereoscopic conversion of a zoom image is illustrated, which is a zoom-in case. As shown in Fig. 12(a), direction of motion vectors is towards outside and magnitudes are also getting larger towards the outside of the image. In the case of a zoom-out image, motion vectors would have the opposite direction. The converted ...
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Citations
We introduce a new stereo matching algorithm that estimates disparities in high-confidence and low-confidence regions separately
. Stereo matching algorithm play an important role in 3D rendering since 3D structures and virtual scenes can be built by disparity map. A complementary tree structure is adopted to identify the high-confidence region and estimate its disparity map using dynamic programming. Then, a disparity fitting algorithm restores the disparities in low-confidence regions using the color and disparity information of high-confidence regions through a global optimization technique. The proposed stereo matching algorithm enhances disparity values in both occlusion and difficult-to-estimate areas (e.g., thin objects), to yield a high quality disparity map.
Many object pointing and selecting techniques for large screens have been proposed in the literature. There is a lack of quantitative evidence suggesting proper pointing postures for interacting with stereoscopic targets in immersive virtual environments. The objective of this study was to explore users' performances and experiences of using different postures while interacting with 3D targets remotely in an immersive stereoscopic environment. Two postures, hand-directed and gaze-directed pointing methods, were compared in order to investigate the postural influences. Two stereo parallaxes, negative and positive parallaxes, were compared for exploring how target depth variances would impact users' performances and experiences. Fifteen participants were recruited to perform two interactive tasks, tapping and tracking tasks, to simulate interaction behaviors in the stereoscopic environment. Hand-directed pointing is suggested for both tapping and tracking tasks due to its significantly better overall performance, less muscle fatigue, and better usability. However, a gaze-directed posture is probably a better alternative than hand-directed pointing for tasks with high accuracy requirements in home-in phases. Additionally, it is easier for users to interact with targets with negative parallax than with targets with positive parallax. Based on the findings of this research, future applications involving different pointing techniques should consider both pointing performances and postural effects as a result of pointing task precision requirements and potential postural fatigue.
Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.
