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Multilevel polygonal descriptor matching defined by combining discrete lines and force histogram concepts

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Abstract

A new method allowing to describe shapes from a set of polygonal curves using a relational descriptor is proposed in this paper. An approach based on discrete lines at several increasing widths is run on the contour of an object to provide a multi-level polygonal representation from accurate description to more and more rough aspects. On each polygon, a force histogram is calculated to define a relational feature signature following a set of directions integrating both spatial relation organization and disparities of the shape in a same distribution. Three different matching schemes are proposed to compare multilevel distributions: global representation, level to level following extracted maxima. This new method is fast and a first experimental study achieved on a common database shows its good behavior.

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References

  1. Baird HS, Tombre K (2014) The evolution of document image analysis. In: Handbook of document image processing and recognition, pp 63–71

  2. Ballard DH (1981) Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122

    Article  MATH  Google Scholar 

  3. Bernier T, Landry J-A (2003) A new method for representing and matching shapes of natural objects. Pattern Recogn 36(8):1711–1723

    Article  Google Scholar 

  4. Cordella LP, Vento M (2000) Symbol recognition in documents: a collection of techniques? Int J Doc Anal Recognit 3(2):73–88

    Article  Google Scholar 

  5. Debled-Rennessona I, Feschetb F, Rouyer-Deglia J (2006) Optimal blurred segments decomposition of noisy shapes in linear time. Comput Graph 30(1):30–36

    Article  Google Scholar 

  6. Debled-Rennesson I, Tabbone S, Wendling L (1981) Multiorder polygonal approximation of digital curves. Electron Lett Comput Vis Image Anal 5(2):98–110

    Google Scholar 

  7. Dilip K, Maylor KHL (2012) Polygonal representation of digital curves. In: Digital image processing InTech

  8. Dosch P, Tombre K, Ah-Soon C, Masini G (2000) A complete system for the analysis of architectural drawings. Int J Doc Anal Recognit 3(2):102–116

    Article  Google Scholar 

  9. Dubois D, Jaulent M-C (1987) A general approach to parameter evaluation in fuzzy digital pictures. Pattern Recogn Lett 6(4):251–259

    Article  MATH  Google Scholar 

  10. Duckham M, Kulik L, Worboys MF, Galton A (2008) Efficient generation of simple polygons for characterizing the shape of a set of points in the plane. Pattern Recogn 41(10):3224–3236

    Article  MATH  Google Scholar 

  11. Hilaire X, Tombre K (2006) Robust and accurate vectorization of line drawings. IEEE Trans Pattern Anal Mach Intell 28(6):890–904

    Article  Google Scholar 

  12. Jain AK, Duin RPW, Maoa J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  13. Janssen RDT, Vossepoel AM (1997) Adaptive vectorization of line drawing images. Comput Vis Image Underst 65(1):38–56

    Article  Google Scholar 

  14. Kerautret B, Lachaud J-O (2012) Meaningful scales detection along digital contours for unsupervised local noise estimation. IEEE Trans Pattern Anal Mach Intell 34(12):2379–2392

    Article  Google Scholar 

  15. Khotanzad A, Hong YH (1990) Invariant image recognition by zernike. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Article  Google Scholar 

  16. Kim W-Y, Kim Y-S (1999) A new region-based shape descriptor, TR15-01, Pisa

  17. Krishnapuram R, Keller JM, Ma Y (1993) Quantitative analysis of properties and spatial relations of fuzzy image regions. IEEE Trans Fuzzy Syst 1(3):222–233

    Article  Google Scholar 

  18. Loncaric S (1998) A survey of shape analysis techniques. Pattern Recogn 31(8):983–1001

    Article  Google Scholar 

  19. Maes M (1991) Polygonal shape recognition using string matching techniques. Pattern Recogn 24(5):443–440

    Article  Google Scholar 

  20. Matsakis P (1998) Relations spatiales structurelles et interprtation dimages. In: PhD Thesis. Université Paul Sabatier, Toulouse

  21. Matsakis P, Wendling L (1999) A new way to represent the relative position between areal objects. IEEE Trans Pattern Anal Mach Intell 21(7):634–643

    Article  Google Scholar 

  22. Matsakis P, Wendling L, Ni J (2010) A general approach to the fuzzy modeling of spatial relationships. In: Jeansoulin R, Papini O, Prade H, Schockaert S (eds) Methods for handling imperfect spatial information, methods for handling imperfect spatial information. Springer, pp 49–74

  23. Mokhtarian F (1995) Silhouette-based isolated object recognition through curvature scale space. IEEE Trans Pattern Anal Mach Intell 17(5):539–544

    Article  Google Scholar 

  24. Nasser H, Ngo P, Debled-Rennesson I (2018) Dominant point detection based on discrete curve structure and applications. J Comput Syst Sci 95:177–192

    Article  MathSciNet  MATH  Google Scholar 

  25. Nguyen TP, Debled-Rennesson I (2011) A discrete geometry approach for dominant point detection. Pattern Recogn 44(1):32–44

    Article  MATH  Google Scholar 

  26. Rusiñol M, Lladós J, Sánchez G (2010) Symbol spotting in vectorized technical drawings through a lookup table of region strings. Pattern Anal Applic 13(3):321–331

    Article  MathSciNet  Google Scholar 

  27. Santosh KC (2018) Document image analysis - current trends and challenges in graphics recognition. Springer, Berlin

    Book  Google Scholar 

  28. Santosh KC, Lamiroy B, Wendling L (2012) Symbol recognition using spatial relations. Pattern Recogn Lett 33(3):331–341

    Article  Google Scholar 

  29. Santosh KC, Lamiroy B, Wendling L (2014) Integrating vocabulary clustering with spatial relations for symbol recognition. Int J Doc Anal Recognit 17(1):61–78

    Article  Google Scholar 

  30. Santosh KC, Wendling L (2015) Graphical symbol recognition. In: Encyclopedia of electrical and electronics engineering. Wiley, pp 1–22

  31. Santosh KC, Wendling L (2018) Angular relational signature-based chest radiograph image view classification. Med Biol Eng Comput 56(8):1447–1458

    Article  Google Scholar 

  32. Sharvit D, Chan J, Tek H, Kimia B (1998) Symmetry-based indexing of image databases. J Vis Commun Image Represent 9(4):366–380

    Article  Google Scholar 

  33. Song J, Su F, Tai C-L, Cai S (2002) An object-oriented progressive-simplification based vectorization system for engineering drawings: model, algorithm, and performance. IEEE Trans Pattern Anal Mach Intell 24(8):1048–1060

    Article  Google Scholar 

  34. Tabbone S, Wendling L (2003) Binary shape normalization using the radon transform. In: International conference on discrete geometry for computer imagery, vol LNCS 2886, pp 184–193

  35. Tabbone S, Wendling L, Tombre K (2003) Matching of graphical symbols in line-drawing images using angular signature information. Int J Doc Anal Recognit 6(2):115–125

    Article  Google Scholar 

  36. Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920–930

    Article  MathSciNet  Google Scholar 

  37. Vacavant A, Roussillon T, Kerautret B, Lachaud J-O (2013) A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours. Comput Vis Image Underst 117(4):438–450

    Article  Google Scholar 

  38. Wendling L, Rendek J, Matsakis P (2008) Selection of suitable set of decision rules using choquet integral. In: Structural, syntactic, and statistical pattern recognition, pp 947–955

  39. Yang S (2005) Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor. IEEE Trans Pattern Anal Mach Intell 27(2):278–281

    Article  Google Scholar 

  40. Zhang D, Lu G (2002) Shape-based image retrieval using generic fourier descriptor. Signal Process 17(10):825–848

    Google Scholar 

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Wendling, L., Debled-Rennesson, I. & Nasser, H. Multilevel polygonal descriptor matching defined by combining discrete lines and force histogram concepts. Multimed Tools Appl 79, 34701–34715 (2020). https://doi.org/10.1007/s11042-019-7531-6

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  • DOI: https://doi.org/10.1007/s11042-019-7531-6

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