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A SetpitextOFF Algorithm-Based Fast Image Projection Analysis

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Advances in Soft Computing and Machine Learning in Image Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 730))

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Abstract

This paper proposes a novel image analysis algorithm called setpitextOFF algorithm for image texture interspacing, retrieving the OFF image pixels, run-length pixels block mapping and image fast projection. To explore the Implementation of Image Analysis, we combine the pixels at different space locations with similar retrieving dependencies as a space vector and mapped the space vectors to form interdependency setpi clusters by context building, these setpi clusters were analysed for the proposed setpitextOFF algorithm. Thereafter, we have formulated a double-setpi cluster to regularize the proposed algorithm implementation in a communication channel. Our proposed algorithm used less time to compute with more accuracy in quality performance metrics comparatively. Our proposed research work can be implemented in any digital communication link, for Video, Image and Data analysis.

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References

  1. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  2. Guo, H., Rangarajan, A., Joshi, S., Younes, L.: Non-rigid registration of shapes via diffeomorphic point matching. IEEE Int. Symp. Biomed. Imag., 924–927 (2004)

    Google Scholar 

  3. Rangarajan, H., Chui, E.M.: A relationship between spline-based deformable models and weighted graphs in non-rigid matching. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 897–904 (2001)

    Google Scholar 

  4. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  5. Yefeng Zheng, D.: Doermann, Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 643–649 (2006)

    Article  Google Scholar 

  6. Maciel, J., Costeira, J.P.: A global solution to sparse correspondence problems. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 187–199 (2003)

    Article  Google Scholar 

  7. Medasani, S., Krishnapuram, R., YoungSik, C.: Graph matching by relaxation of fuzzy assignments. IEEE Trans. Fuzzy Syst. 9(1), 173–182 (2001)

    Article  Google Scholar 

  8. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  9. Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2003)

    Google Scholar 

  10. Yefeng Zheng, D.: Doermann, Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 643–649 (2006)

    Article  Google Scholar 

  11. Jiang, H., Drew, M.S., Li, Z.-N.: Matching by linear programming and successive convexification. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 959–975 (2007)

    Article  Google Scholar 

  12. Stark, H., Yang, Y.: Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets and Optics. Wiley, New York (1998)

    MATH  Google Scholar 

  13. Youla, D.C.: Mathematical theory of image restoration by the method of convex projections, Chapter 2. In: Stark, H. (ed.) Image Recovery: Theory and Applications. Academic Press, Orlando, Florida (1987)

    Google Scholar 

  14. Treisman, A.: A feature in integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)

    Article  MathSciNet  Google Scholar 

  15. Julesz, B.: Textons, the elements of texture perception and their interactions. Nature 290(5802), 91–97 (1981)

    Article  Google Scholar 

  16. Julesz, B.: Texton gradients: the texton theory revisited. Biol. Cybern. 54, 245–251 (1986)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Sreenidhi Institute of Science and Technology, MEC, Yanampet, Ghatkesar, Hyderabad, Telangana, India

    V. Kakulapati & Vijay Pentapati

Authors
  1. V. Kakulapati
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  2. Vijay Pentapati
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Corresponding author

Correspondence to V. Kakulapati .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. CUCEI, Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico

    Diego Alberto Oliva

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Kakulapati, V., Pentapati, V. (2018). A SetpitextOFF Algorithm-Based Fast Image Projection Analysis. In: Hassanien, A., Oliva, D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-63754-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63753-2

  • Online ISBN: 978-3-319-63754-9

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