MATLAB Program to convert 2D image to 3D image
MATLAB Programming for image conversion step by step
Why 2D to 3D image conversion is needed ???
- 3D displays provide a dramatic improvement of visual quality than the 2D displays.
- For existed 2D contents, depth information is not recorded.
- The idea of developing such a technique is to eliminate the dependency of images
Deals with applying spatial transformation on the image. The entire process is divided into 4 stages:-
Stage I – Perspective Projection/Projection of the image:
It involves projecting out the grid points on the image. For projecting out these points, the system involves the technique of projective translation.
Stage II – Frame Structuring of projective image:
Stage III – Transparency Adjustment:
It involves varying the transparency level of the frame structured projective image with respect to standard opacity. The system selects the transparency value such that there is no pixel desensitisation and no motion tearing such that quality of the image is restored.
Stage IV – Positional Coordination / Overlaying:
The image contains two same coordinates so that the depth of vision remains fixed and doesn‘t account for distortion of image.
MATLAB PROGRAMMING:
ADVANTAGES OF SINGLE INPUT :
The most important application is that it requires only SINGLE INPUT unlike other systems which needs 2 images by default.
It doesn’t require polarized glasses. The striking feature of this system is that though there is no polarisation of image involved, the 3D image can be seen through the MATLAB GUI directly.
Due to optimisation, there is not motion tearing and pixel desensitisation. Thus no vision blurring.
There is no parallax error seen while viewing the converted image.
It eradicates the use of two sources to capture an image.
It doesn’t require polarized glasses. The striking feature of this system is that though there is no polarisation of image involved, the 3D image can be seen through the MATLAB GUI directly.
Due to optimisation, there is not motion tearing and pixel desensitisation. Thus no vision blurring.
There is no parallax error seen while viewing the converted image.
It eradicates the use of two sources to capture an image.
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ReplyDeleteVery interesting! I need ur testing images to test ....if u r ok, pls send it via mail.... mahaeti.aprilmoe@gmail.com
ReplyDeleteThanks
Please download from blog.
Deletecode ?
ReplyDeleteSee in the video
DeleteHi,
ReplyDeleteI'm having some typos when trying to repeat the code for myself from the video. Could you please make the .M file available for download?
You have to mail us.. castorclasses2014@gmail.com
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ReplyDeleteHi, please can I get a program that can serve as a master's project thesis. I am a master's student in highway and transportation engineering.
ReplyDeletePlease mail us your requirements castorclasses2014@gmail.com
DeleteI think that it's very interesting your code and I really need it but I can't find the link to download it. Please, could you send me by email??
ReplyDeleteThanks!!
jsg28 November 2018 at 00:10
ReplyDeleteI think that it's very interesting your code and I really need it but I can't find the link to download it. Please, could you send me by email??
Can you please let us know how to download the code. I could not find it on the blog. kindly email it to me at mahmud.muhamm1@gmail.com
ReplyDeleteHi,
ReplyDeleteCan i get this code?
require the code
ReplyDelete