Accomplishing 2D Video to 3D Video

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The main purpose of this project is to accomplish 2D video to 3D video. The depth map extraction and 3D image warping stereo image.
For the extraction of the depth map includes three parts, image block motion Extraction, color segmentation, Depth map average fusion. the propose of image block motion Extraction is to use 2D image of foreground and background which extract the predominant lines and vanishing point of the background base on geometry method and motion, the background depth map is considered. The depth of the object have used the motion extraction to obtain the motion depth map and then divided according to the color of the object to give the object region segmentation depth map.
Then, to analyze the basic principles of stereoscopic vision of human eyes, thus, to analyze the depth map generation.

2. Introduction and Overview
With the rapid development of technology, three-dimensional video is becoming increasingly prevalent in our lives. Stereoscopic video to give the viewer a more intense visual stimulation, increased realism. 3D technology enables sophisticated 3D movies get better returns than 2D movies. And now, more and more researchers in Europe focused on the development of three-dimensional TV. In addition, many TV manufacturers launched 3D TV market, so that people can enjoy watching 3D video at home. All the facts are proved, 3D dynasties come faster than we expected.
The 2D video to 3D has great practical significance. First, it can lead to more intense visual stimulation to the audience, to increase realism. Secondly, 2D to 3D 3D video capture is also able to reduce costs in the process, realize the possibilities of ordinary camcorders shoot 3D video. In addition, 2D to 3D, 3D video will meet the need...

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...th, im.nChannels)

#
# This would be easier if we had COI support for cv.Set, but it doesn't
# work that way.
# OpenCV uses BGR order (even if input image is greyscale):
# http://www.cs.iit.edu/~agam/cs512/lect-notes/opencv-intro/opencv-intro.html
# red goes on the left, cyan on the right:
# http://en.wikipedia.org/wiki/Anaglyph_image
#
b = cv.CreateImage(size, im.depth, 1) g = cv.CreateImage(size, im.depth, 1) r = cv.CreateImage(size, im.depth, 1) cv.Split(im, b, g, r, None)

zeros = cv.CreateImage(size, r.depth, 1) cv.Merge(zeros, zeros, r, None, left) cv.Merge(b, g, zeros, None, right)

#
# cvRect is ( x, y, width, height ) and it MUST be a tuple, not a list
#
cv.SetImageROI(left, ( SHIFT, 0, width - SHIFT, height )) cv.SetImageROI(right, ( 0, 0, width - SHIFT, height ))

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