Convert 2D Images to 3D Image

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dc.contributor.authorGaspe, G.M.S.M.
dc.contributor.authorEkanayake, R.M.T.C.B.
dc.contributor.authorWijesooriya, P.N.
dc.date.accessioned2022-03-21T10:34:11Z
dc.date.available2022-03-21T10:34:11Z
dc.date.issued2013
dc.description.abstractImage processing plays a major role in modern technology. Implementations of this field are varying from simple applications like documentation to complex applications like space context. Before use in the above applications images should be processed using relevant techniques. There are many software that can process the images and get the outputs to be used in the above mentioned applications. Conversion of 2D images into 3D format is essential in applications such as automatic navigation of robots and vehicles, satellite identification and fault diagnosis, medical reasoning and remote surgery (Pollefeys et al., 2000). With developed software user can obtain 3D images using 2D images in a lesser time and the human interaction have been eliminated to a single step. Methodology MATLAB is a technical computing language used throughout the research (Background on matlab image processing toolboxes, 2013). The system allows user to input two stereo images of the object which is to be converted into 3D. Then the image pair is read and converted to gray scale for the matching process. To have more efficient and accurate results two system objects were used. Basically red-cyan channel images give 3D visualization when visualized with 3D spectacles or one with a red and cyan vision. So the conversion of two stereo images to cyan and red is done. RGB value of cyan is 0,255,255 and RGB value of red is 255, 0, 0. So left image is (:,:,1:1)=0 and right image is(:,:,2:3)=0;. For the best block matching step, extract the 7 X 7 pixel block is considered in the right image and search along the same row in the left image (Image point matching, 2013). Sum of absolute difference is used to compare the image regions. Template Matcher System object is used to increase the accuracy of blocks matching (www.imageprocessingbook.com). Then allocating space for all template matcher system objects is done. Scan is done over all rows and columns and disparity bound is calculated for each. Then template and region of interest is constructed. Depth map have positive values only. Finally As there are noisy patches and bad depth estimates everywhere noise filtration is done.en_US
dc.identifier.urihttp://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/8538/03-SCT-Convert%202D%20Images%20to%203D%20Image%20.pdf?sequence=1&isAllowed=y
dc.language.isoenen_US
dc.publisherUva Wellassa University of Sri Lankaen_US
dc.subjectScience and Technologyen_US
dc.subjectTechnologyen_US
dc.subjectRoboticsen_US
dc.subjectSoftware Developingen_US
dc.subjectComputer Scienceen_US
dc.subjectModern Technology.en_US
dc.titleConvert 2D Images to 3D Imageen_US
dc.title.alternativeResearch Symposium 2013en_US
dc.typeOtheren_US
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