a novel 2d-to-3d conversion system using edge information
DESCRIPTION
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. Introduction. Some approaches that can generate 3D content Time-of-flight depth sensor Triangular stereo vision 3D graph rendering. - PowerPoint PPT PresentationTRANSCRIPT
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A Novel 2D-to-3D Conversion System Using
Edge Information
IEEE Transactions on Consumer Electronics 2010Chao-Chung Cheng
Chung-Te liLiang-Gee Chen
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IntroductionSome approaches that can generate 3D content
Time-of-flight depth sensorTriangular stereo vision3D graph rendering
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IntroductionHow does our brain perceive depth?
Monocular cues: one of the major categories for depth perception
Motion parallax
Binocular cues
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Monocular cuesInterposition (overlapping)
Relative Height
Familiar Size
Texture Gradient
Shadow
Linear Perspective
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Proposed System
Block-Based Region GroupingDepth from Prior Hypothesis3D 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 RenderingRemove 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 ComplexityAnalysis 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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Analysis of Visual Quality
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Analysis of Visual QualityComparing the depth quality and visual comfort
over 4 video data typesVideos that captured by a stereoscopic cameraProposed algorithmPrevious work of [9]Commercial software of DDD’s TriDef
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Analysis of Visual Quality
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ConclusionThe 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.