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Reseach Article

A Novel Canny Mean Restoration Algorithm for Correspondence and Motion Tracking in Dynamic Image Sequence Analysis

by G. S. Yogananda, Y. P. Gowramma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 5
Year of Publication: 2017
Authors: G. S. Yogananda, Y. P. Gowramma
10.5120/ijca2017913189

G. S. Yogananda, Y. P. Gowramma . A Novel Canny Mean Restoration Algorithm for Correspondence and Motion Tracking in Dynamic Image Sequence Analysis. International Journal of Computer Applications. 161, 5 ( Mar 2017), 12-18. DOI=10.5120/ijca2017913189

@article{ 10.5120/ijca2017913189,
author = { G. S. Yogananda, Y. P. Gowramma },
title = { A Novel Canny Mean Restoration Algorithm for Correspondence and Motion Tracking in Dynamic Image Sequence Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 5 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number5/27144-2017913189/ },
doi = { 10.5120/ijca2017913189 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:39.914200+05:30
%A G. S. Yogananda
%A Y. P. Gowramma
%T A Novel Canny Mean Restoration Algorithm for Correspondence and Motion Tracking in Dynamic Image Sequence Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 5
%P 12-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a novel computationally efficient canny mean restoration algorithm for correspondence to identify the matching of the similar features between the reference image frames to the set of search image frames in the dynamic image sequence analysis for tracking motion object in the sequence. This restoration and correspondence has three major steps such as segmentation, feature extraction for restoration, and matching. This paper proposes block-based segmentation in which the reference image frame which contains the object to be tracked can be blocked as square window of size m*m which covers the object. The next step is the feature extraction for restoration. Here we have considered the block based features in which we calculate the canny edge mean of the region of interest. These features are invariant motion blur and noise of motion deblurring, denioseing and it reduces the dimensionality and finally for matching we made use of the minimum absolute similarity distance measure for these features of the blocks. The searching space is restricted to [-15,+15] pixels in horizontal ,vertical and diagonal directions in the search image frames. The performance of the algorithm is presented for without preprocessed for slow moving objects of various sequences.

References
  1. Jong-Ho Lee, Yo-Sung Ho, “High Quality Non Blind Image Deconvolution with Adaptive RegularizationImage”, J. Vis. Comm. Image R.22,2011, pp.: 653-663.
  2. D.SrinivasaRao, K.SelvaniDeepthi,K. MoniSushma Deep, “Application of Blind Deconvolution and Application for Image Restoration”, International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 3 March 2011, pp: 1878-1884.
  3. AmanpreetKaur, Hitesh Sharma,“Restoration of MRI Images with various types of techniques and Compared with Wavelet Transform”,CPMR-IJT, Vol. 2, No. 1, June 2012, pp: 6-12.
  4. BI Xiao-jun, WANG Ting, “Adaptive Blind Image Restoration Algorithm of Degraded Image”, IEEECongress on Image and Signal Processing CISP '08, Vol. 3, 2008, pp: 536-540.
  5. Mr. Salem Saleh Al-amri , Dr. N.V. Kalyankar, “A Comparative Study for Deblurred Average Blurred Images”,(IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 03, 2010, pp: 731-733
  6. Yuanxiang Li, Na Li,“Image Restoration using Improved Particle Swarm Optimization”,International Conference on Network Computing and Information Security, 2011, pp:394-397.
  7. Feng-qing Qin, Jun Min, Hong-rongGuo, “A blind Restoration Method based on PSF Estimation”, IEEEWRI World Congress on Software Engineering WCSE '09, Vol. 2, 2009, pp: 174-176.
  8. Ming Yan,“Restoration of images corrupted by Impulse Noise Using Blind Inpaintingand l0 norm”, Preprint, November 7,2011, pp: 1-14.
  9. S. DerinBabacan, Rafael Molina,Aggelos K.Katsaggelos, “Sparse Bayesian Image Restoration”,17thIEEE International Conference on Image Processing (ICIP),Sep. 26-29, 2010, pp:3577-3580.
  10. Hanyu Hong , Liangcheng Li, Luxin Yan, TianxuZhang,“Unified Restoration Method for Different Degraded Images”, IEEE International Conference on Optoelectronics and Image Processing (ICOIP),Vol. 2, 2010, pp: 714-717.
  11. Archee NAZ, AnjanTaludar, Kanarpa Kumar Sarma, “Digital Image Restoration using Discrete Wavelet Transform Based approach”,IRNet Transactions on Electrical and Electronics Engineering (ITEEE), Vol-1, Iss-2, 2012, pp:53-57.
  12. Liu Yang-Yang, Jin Wei-qi, “Super-Resolution Image Restoration based on Orthogonal Discrete Wavelet Transform”, Proc. of SPIE, Vol. 5637,2005, pp: 203-211.
  13. A. Prochazka, J. Ptacek, I. Sindelarova, “Wavelet Transform in Signal and Image Restoration”, In Proceedings of Conference CONTROL, 2004, pp:1-5.
  14. Sun qi, Hongzhi Wang, Lu Wei, “An Iterative Blind Deconvolution Image Restoration algorithm based on Adaptive Selection of Regularization Parameters”, 3rdIEEE International Symposium on Intelligent Information Technology Application,Vol. 1, IITA,2009,pp:112-115.
  15. Ashwini M. Deshpande, SupravaPatnaik, “Comparative study and Qualitative-Quantitative Investigations of Several Motion Deblurring algorithm”, 2nd International Conference and workshop on Emerging Trends in Technology (ICWET), No. 2, 2011, pp: 27-34.
  16. Stuart W. Perry, Ling Guan, “Perception based Adaptive Image restoration”, IEEE Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference, Vol.5, 1998,pp:2893-296.
  17. Yong Ge, Qiuming Cheng, “Boundary Effect Reduction in Image Filtering”, ICGST International Journal on Graphics, Vision and Image Processing GVIP journal, Vol. 7, Issue 2, Aug 2007, pp: 17-25.
  18. RyuNagayasu, Naoto Hosoda, Nari Tanabe, Hideaki Matsue, Toshihiro Furukawa, “Restoration method for Degraded Images using Two-Dimensional Block Kalman Filter with Colored Driving Source”, Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop DSP/SPE,2011, pp:151-156.
  19. Taeg Sang Cho, C. Lawrence Zitnick, Neel joshi,, Sing Bing Kang, Richard Szeliski, William T. Freeman, “Image Restoration by Matching Gradient Distributions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 4,April 2012, pp. 683 – 694.
  20. Sunghyun Cho, Seungyong Lee, “Fast Motion Deblurring”, ACM Transactions on Graphics(TOG), Vol.28, No.5, Article 145, Dec. 2009, pp: 145:1-145:8.
  21. Videre Design-Small Vision System”,http://www.videre design.com/vision/svs.htm,2009.
  22. S.Birchfield and C.Tomasi ,”A Pixel Dissimilarity Measure that is Insensitive     image   Sampling”, IEEE      Trans.Pattern Analysis and Machine Intelligence,vol.20,  no.4, pp  401-406,Apr.1998.
  23. B.Lofy,”High accuracy Registration and Targeting”, Proceedings of the 29th applied      imagery pattern     recognition workshop, oct. 16-18,2000,pp 235-241.
  24. Serge Beucher,”Watershed, Hierarchical Segmentation And Waterfall Algorithm”.
  25. Hidetomo Sakaino and Xiqun Lu,”Dynamic Edge Detection And Mutiple Frame Based  Derivative     Tensor”, pp 2161-2163, ICIP 2009.
  26. L.Brown. A survey of image registration techniques. In ACM Computing Surveys, volume 24, pages 325-      375, new York, 1992, ACM Press.
  27. Zullfiqar Hasan khan and Irene Yu-Hua Gu, ”Joint Feature Correspondence and Appearance Similarity for      Robust Object Tracking” IEEE Transac. On information forensic and security, 5(3)September 2010.
  28. Zhengyou Zhang Olivier Faugeras,” 3D Dynamic scene Analysis- A stereo Based   Approach,” Sprenger-   Verlag.
  29. Juyang Weng, Thomas S Huang, Narendra Abuja,” Motion and structure from image Sequence,” Springler-Verlag.
  30. Edited by S.Levioldi,” Multi Computer Vision,” Academic press1988
  31. Ting Junfan,” Describing a Recognizing 3-D objects using surface properties.”
  32. Hans-Hermann Bock, Admin Diday (Eds),” Analysis of Symbolic Data,” Springer.
  33. Earl Goes, Richard Johnsonbaugh, Steve Jost,” Pattern recognition and image Analysis.”
  34. Laurent Mallet. “Structural Methods in Pattern Recognition,” North Oxford Academic.
  35. Jean-Claude Simon, “Patterns and Operators the foundation of Data Representation,” North oxford Academic.
  36. Richard O Duda, PeterE. Hart, David D.Stork, “Pattern Classification” Second Edition, Awiley Inter Science Publication.
  37. Menehem Friendman, Abraham, Kandel,” Introduction to Pattern Recognition,”World Scientific.
  38. Robert J.schalkoff, “Patter recognition, Statistical, Structural and Neural approaches,” Springer.
  39. Dutta and Mazumdar “Digital Image processing and analysis”.
  40. J.P.Marquesdasa,” Pattern Recognition, Statistical, Structural and Neural Approaches,” Springer.
  41. Milan Sonok, V aclav Hlavac Roger Boyle,” Image Processing, Analysis and Machine vision,” Second        edition, PWS publisher.
  42. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Addison Wesley
Index Terms

Computer Science
Information Sciences

Keywords

Canny Mean Restoration Correspondence Motion tracking.