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Reseach Article

3D Image Reconstruction using Raspberry Pi

by Shruti Rayaji, Udaykumar L. Naik
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 4
Year of Publication: 2018
Authors: Shruti Rayaji, Udaykumar L. Naik
10.5120/ijca2018917388

Shruti Rayaji, Udaykumar L. Naik . 3D Image Reconstruction using Raspberry Pi. International Journal of Computer Applications. 181, 4 ( Jul 2018), 8-13. DOI=10.5120/ijca2018917388

@article{ 10.5120/ijca2018917388,
author = { Shruti Rayaji, Udaykumar L. Naik },
title = { 3D Image Reconstruction using Raspberry Pi },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 181 },
number = { 4 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number4/29702-2018917388/ },
doi = { 10.5120/ijca2018917388 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:58.404282+05:30
%A Shruti Rayaji
%A Udaykumar L. Naik
%T 3D Image Reconstruction using Raspberry Pi
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 4
%P 8-13
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes 3 dimensional image reconstruction captured of different objects angled differently. Raspberry Pi camera is used to capture the images. The main objective of this paper lies in using the two dimensional images in the form of cross sectional slides also known as multi slice-to-volume registration that are placed one above the other in a mesh to re-project it back to a three dimensional model. Iterative reconstruction algorithm is applied as it is insensitive to noise and even in case of incomplete data or image it helps in reconstructing the image in the most favorable manner. Finally, the experiments are compared with the other methods or algorithms such as Speeded up robust feature (SURF) and Sum of squared differences (SSD) methods which are far more complex with nominal clarity.

References
  1. Zhang, Z. “A Flexible New Technique for Camera Calibration.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 22, No. 11, 2000, pp. 1330–1334.
  2. Heikkila, J., and O. Silven. “A Four-step Camera Calibration Procedure with Implicit Image Correction.” IEEE International Conference on Computer Vision and Pattern Recognition.1997.
  3. http://www.vision.caltech.edu/bouguetj/calib_docfor, Camera Calibration
  4. Trucco, emanuele and verri, “Introductory Techniques for 3D Computer vision”.
  5. Viral H.Borisagar, Mukesh A.Zaveri, “A Novel Segment based Stereo Matching algorithm for Disparity map GenerationInternational conference on Computer and Software Modeling, Volume 14, 2011.
  6. Sahitya S, Lokesha H, Sudha L K “Real time application of Raspberry Pi in compression of images”, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).
  7. Luis Gerardo de la Fraga and Israel Vite Silva “Direct 3D Metric Reconstruction from Two Views Using Differential Evolution”, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
  8. Chuan Li, injin Zheng, Chuangyin Dang and Hongjun Zhou “A Method of 3D Reconstruction from Image Sequence”, 2009 2nd International Congress on Image and Signal Processing.
Index Terms

Computer Science
Information Sciences

Keywords

Raspberry Pi features camera 2d images cross sectional slice-to-volume registration Iterative reconstruction.