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A Novel 2D-to-3D Conversion System Using Edge Information IEEE Transactions on Consumer Electronics 2010 Chao-Chung Cheng Chung-Te li Liang-Gee Chen
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Introduction Some approaches that can generate 3D content Time-of-flight depth sensor Triangular stereo vision 3D graph rendering
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Introduction How does our brain perceive depth? Monocular cues : one of the major categories for depth perception Motion parallax Binocular cues
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Monocular cues Interposition (overlapping) Relative Height Familiar Size Texture Gradient Shadow Linear Perspective
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Proposed System Block-Based Region Grouping Depth from Prior Hypothesis 3D Image Visualization using Bilateral Filtering and Depth Image-Based Rendering
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Proposed 2D-to-3D Conversion System
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Block-Based Region Grouping 1. Measure the similarity of neighboring blocks 2. The blocks are segmented into multiple groups by MST
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Depth from Prior Hypothesis 1. Use a line detection algorithm[9] to detect the linear perspective of the scene C.-C. Cheng, C.-T. Li, P.-S. Huang, T.-K. Lin, Y.-M. Tsai, and L.-G. Chen, “A block-based 2D-to-3D conversion system with bilateral filter,” in Proc. IEEE Int. Conf. Consumer Electronics, 2009
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Depth from Prior Hypothesis 2. Find the corresponding depth map gradients 3. Compute the gravity center of the block group as the depth
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3D Image Visualization using Bilateral Filtering and Depth Image-Based Rendering Remove the blocky artifacts by cross bilateral filter Then the depth map is used to generate 3D image by DIBR[3] W.-Y. Chen and Y.-L. Chang and S.-F. Lin and L.-F. Ding and L.-G. Chen, “Efficient depth image based rendering with edge dependent depth filter and interpolation,” in Proc. ICME, pp. 1314-1317, 2005
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Experiment Result Analysis of Computational Complexity Analysis of Visual Quality
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Analysis of Computational Complexity The computational complexity is Larger block size implies shorter computational time but lower depth map quality
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Analysis of Visual Quality
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Comparing the depth quality and visual comfort over 4 video data types Videos that captured by a stereoscopic camera Proposed algorithm Previous work of [9] Commercial software of DDD’s TriDef
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Analysis of Visual Quality
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Conclusion The proposed algorithm uses edge information to group the image into coherent regions. A simple depth hypothesis is determined by the linear perspective of the scene. The algorithm is quality-scalable depending on the block size.
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