2009 Third International Symposium on Intelligent Information Technology Application 2009
DOI: 10.1109/iita.2009.481
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A Depth Extraction Method Based on Motion and Geometry for 2D to 3D Conversion

Abstract: With the development of 3DTV, the conversion of existing 2D videos to 3D videos becomes an important component of 3D content production. One of the key steps in 2D to 3D conversion is how to generate a dense depth map. In this paper, we propose a novel depth extraction method based on motion and geometric information for 2D to 3D conversion, which consists of two major depth extraction modules, the depth from motion and depth from geometrical perspective. The H.264 motion estimation result is utilized and coop… Show more

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Cited by 42 publications
(25 citation statements)
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“…Vanishing line detection [10][11][12][13] Depth from model Color theory [14,15,23,34] Depth from defocus Use blur information to get depth value [16][17][18][19] Depth from visual saliency Estimation in region of interest [20][21][22] 5. Four kinds of scanning modes to fix the depth map; 6.…”
Section: Overview Of the Proposed Methodsmentioning
confidence: 99%
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“…Vanishing line detection [10][11][12][13] Depth from model Color theory [14,15,23,34] Depth from defocus Use blur information to get depth value [16][17][18][19] Depth from visual saliency Estimation in region of interest [20][21][22] 5. Four kinds of scanning modes to fix the depth map; 6.…”
Section: Overview Of the Proposed Methodsmentioning
confidence: 99%
“…In [20] they set up a method based on visual saliency to extract the regions of interest from a color image. Additional methods have applied a combination of several depth cues to determine the depth information, such as in [3,5,6]. Jung et al [23] proposed a depth-map estimation algorithm under the assumption that the depth of image will increase from bottom region to upper region.…”
Section: Introductionmentioning
confidence: 99%
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“…An extension to the general segmentation method is over-segmentation [35,42] or the use of high level object segmentation [43][44][45] often based on human intervention [15]. Another approach to improving robustness is using structures within images [46] to help validate depth assignments by other methods.…”
Section: Geometric Assignmentmentioning
confidence: 99%