• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Role Of Pattern Characteristics In Cross Correlation Based Motion Estimation
  • VIEWS 939
  • PDF 208

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i41.1137

Year: 2021, Volume: 14, Issue: 41, Pages: 3114-3125

Original Article

Role Of Pattern Characteristics In Cross Correlation Based Motion Estimation

Hema Tekwani1*, Krishna Raj2

1Research Scholar, Electronics Engineering Department, Harcourt Butler Technical University, 208002, India
2Professor, Electronics Engineering Department, Harcourt Butler Technical University,208002, India

*Corresponding Author
Email: [email protected]

Received Date:21 June 2021, Accepted Date:16 November 2021, Published Date:06 December 2021

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To establish a pattern tracking based motion estimation algorithm for the stereovision based system and to investigate the effect of threshold value (Thv), size and population of patterns on the pattern tracking value. Methods: Proposed motion estimation algorithm correlates the set of motion frames captured from two high speed cameras configured in stereovision system. The correlation scheme was based on grayscale pattern tracking in moving frames. Pattern development and correlation algorithms were developed. A spherical object was given small random displacements and the motion was captured using stereovision system. The effectiveness of algorithm is evaluated with the pattern tracking value which should be close to 1 for perfect pattern match. Findings: The correlation results indicate that pattern tracking value were found to be 0.920 and 0.899 for left and right cameras respectively when the threshold value (>10) and size 10 pixels are considered with high population (200 patterns). With the increase in pattern size, the pattern tracking value decreased. Another study revealed that pattern tracking value was comparatively higher when the pattern population was maximum (200 patterns). The pattern tracking value again decreased when the threshold value (>15) is considered. It was concluded that pattern size of 10 pixels with threshold value (>10) is more pronounced for motion estimation. The proposed algorithm is verified for the 3D displacements of a rectangular plate mounted on XYZ translation stage. The pattern tracking values were 0.97, 0.95, 0.96 and 0.96, 0.94, 0.95 for X, Y and Z displacements respectively. The correlation algorithm is also coupled with the compression technique using wavelet based data compression. Novelty/Applications: The proposed algorithm can be efficiently applied for both in plane and out of plane motion estimation. The algorithm can provide constructive outcomes for small motion prediction with proper selection of pattern size and threshold value.

Keywords: Camera calibration; Image acquisition; Image correlation; Motion estimation; Pattern tracking; Compression

References

  1. Shin J, Kim S, Kang S, Lee SW, Paik J, Abidi B, et al. Optical flow-based real-time object tracking using non-prior training active feature model. Real-Time Imaging. 2005;11(3):204–218. Available from: https://dx.doi.org/10.1016/j.rti.2005.03.006
  2. Keller Y, Averbuch A. Global parametric image alignment via high-order approximation. Computer Vision and Image Understanding. 2008;109:244–259. Available from: https://dx.doi.org/10.1016/j.cviu.2007.05.003
  3. Horn BKP, Schunck BG. Determining optical flow. Artificial Intelligence. 1981;17(1-3):185–203. Available from: https://dx.doi.org/10.1016/0004-3702(81)90024-2
  4. Kim YH, Martínez AM, Kak AC. Robust motion estimation under varying illumination. Image and Vision Computing. 2005;23(4):365–375. Available from: https://dx.doi.org/10.1016/j.imavis.2004.05.010
  5. Brox T, Malik J. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011;33(3):500–513. Available from: https://dx.doi.org/10.1109/tpami.2010.143
  6. Baker S, Matthews I. Lucas-Kanade 20 Years On: A Unifying Framework. International Journal of Computer Vision. 2004;56(3):221–255. Available from: https://dx.doi.org/10.1023/b:visi.0000011205.11775.fd
  8. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing. 2004;13(4):600–612. Available from: https://dx.doi.org/10.1109/tip.2003.819861
  9. Drulea M, Nedevschi S. Motion Estimation Using the Correlation Transform. IEEE Transactions on Image Processing. 2013;22(8):3260–3270. Available from: https://dx.doi.org/10.1109/tip.2013.2263149
  10. Sutton MA, Orteu JJ, Schreier HW. Image Correlation for Shape, Motion and Deformation Measurements. Springer. 2009. 10.1007/978-0-387-78747-3
  11. Solav D, Moerman KM, Jaeger AM, Genovese K, Herr HM. MultiDIC: An Open-Source Toolbox for Multi-View 3D Digital Image Correlation. IEEE Access. 2018;6:30520–30535. Available from: https://dx.doi.org/10.1109/access.2018.2843725
  12. Nguyen H, Kieu H, Wang Z, Le HND. Three-dimensional facial digitization using advanced digital image correlation. Applied Optics. 2018;57(9):2188. Available from: https://dx.doi.org/10.1364/ao.57.002188
  13. Vedula S, Rander P, Collins R, Kanade T. Three-dimensional scene flow. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005;27(3):475–480. Available from: https://dx.doi.org/10.1109/tpami.2005.63
  14. Siebert T, Wood R, Splitthof K. High speed image correlation for vibration analysis. Journal of Physics: Conference Series. 2009;181:012064. Available from: https://dx.doi.org/10.1088/1742-6596/181/1/012064
  15. Wang X, Pan Z, Fan F, Wang J, Liu Y, Mao SX, et al. Nanoscale Deformation Analysis With High-Resolution Transmission Electron Microscopy and Digital Image Correlation. Journal of Applied Mechanics. 2015;82(12). Available from: https://dx.doi.org/10.1115/1.4031332
  16. Sutton M, Mingqi C, Peters W, Chao Y, McNeill S. Application of an optimized digital correlation method to planar deformation analysis. Image and Vision Computing. 1986;4(3):143–150. Available from: https://dx.doi.org/10.1016/0262-8856(86)90057-0
  17. Gao Y, Cheng T, Su Y, Xu X, Zhang Y, Zhang Q. High-efficiency and high-accuracy digital image correlation for three-dimensional measurement. Optics and Lasers in Engineering. 2015;65:73–80. Available from: https://dx.doi.org/10.1016/j.optlaseng.2014.05.013
  18. Sun Y, Pang JHL, Wong CK, Su F. Finite element formulation for a digital image correlation method. Applied Optics. 2005;44(34):7357. Available from: https://dx.doi.org/10.1364/ao.44.007357
  19. Blaber J, Adair B, Antoniou A. Ncorr: Open-Source 2D Digital Image Correlation Matlab Software. Experimental Mechanics. 2015;55(6):1105–1122. Available from: https://dx.doi.org/10.1007/s11340-015-0009-1
  20. Reu P. Calibration: a good calibration image. Experimental Techniques. 2013;37:1–3. Available from: doi.org/10.1111/ext.12059
  21. Tekwani H, Raj K. Brightness Intensity-Based Transient Motion Prediction. In: Smart Systems: Innovations in Computing. (pp. 301-310) Springer Singapore. 2022.
  22. Reu P. Stereo-rig design: Lighting- part 5. Experimental Techniques. 2013;37(1-2):1–3. Available from: doi.org/10.1111/ext.12059
  23. Long X, Fu S, Qi Z, Yang X, Yu Q. Digital Image Correlation Using Stochastic Parallel-Gradient-Descent Algorithm. Experimental Mechanics. 2013;53(4):571–578. Available from: https://dx.doi.org/10.1007/s11340-012-9667-4
  24. Reu P. 2015.
  25. Baker S, Scharstein D, Lewis JP, Roth S, Black MJ, Szeliski R. A Database and Evaluation Methodology for Optical Flow. International Journal of Computer Vision. 2011;92(1):1–31. Available from: https://dx.doi.org/10.1007/s11263-010-0390-2
  27. Monin S, Hahamovich E, Rosenthal A. Single-pixel imaging of dynamic objects using multi-frame motion estimation. Scientific Reports. 2021;11(1). Available from: https://dx.doi.org/10.1038/s41598-021-83810-z
  28. Stamm MC, Liu KJR. Anti-forensics of digital image compression. IEEE Transactions on Information Forensics and Security. 2011;6(3):1050–1065. Available from: https://dx.doi.org/10.1109/tifs.2011.2119314
  30. Tekwani H, Raj K. Compression using Thresholding on Signal/Image by applying Wavelet Analysis. 2018 International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES). 2018.
  31. Prasad PMK, Prasad DYV, Rao GS. Performance Analysis of Orthogonal and Biorthogonal Wavelets for Edge Detection of X-ray Images. Procedia Computer Science. 2016;87:116–121. Available from: https://dx.doi.org/10.1016/j.procs.2016.05.136
  33. Reu P. Calibration: stereo calibration. Experimental Techniques. 2014;38:1–2. Available from: doi.org/10.1111/ext.12048

Copyright

© 2021 Tekwani & Raj. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

  • 06 December 2021

Year: 2021, Volume: 14, Issue: 41

Article Metrics


Post Graduate Fellowship in Research Communication

More articles

DON'T MISS OUT!

Subscribe now for latest articles and news.