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Reseach Article

Scaling of Color Image using B-spline Curves

Published on September 2015 by P. J. Kulkarni, and U. B. Hatwar
Emerging Applications of Electronics System, Signal Processing and Computing Technologies
Foundation of Computer Science USA
NCESC2015 - Number 1
September 2015
Authors: P. J. Kulkarni, and U. B. Hatwar
65d600bd-80cd-4060-b2ae-aeb435a5a6e4

P. J. Kulkarni, and U. B. Hatwar . Scaling of Color Image using B-spline Curves. Emerging Applications of Electronics System, Signal Processing and Computing Technologies. NCESC2015, 1 (September 2015), 5-8.

@article{
author = { P. J. Kulkarni, and U. B. Hatwar },
title = { Scaling of Color Image using B-spline Curves },
journal = { Emerging Applications of Electronics System, Signal Processing and Computing Technologies },
issue_date = { September 2015 },
volume = { NCESC2015 },
number = { 1 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 5-8 },
numpages = 4,
url = { /proceedings/ncesc2015/number1/22359-7323/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Applications of Electronics System, Signal Processing and Computing Technologies
%A P. J. Kulkarni
%A and U. B. Hatwar
%T Scaling of Color Image using B-spline Curves
%J Emerging Applications of Electronics System, Signal Processing and Computing Technologies
%@ 0975-8887
%V NCESC2015
%N 1
%P 5-8
%D 2015
%I International Journal of Computer Applications
Abstract

This paper presents representation techniques as the image can be represented in Cubic B-splines that are used to represent the curvilinear features of an image. The algorithm is devised to convert a raster image into vector image. The algorithm first detects the curvilinear features of the image, then based on the curvilinear edges and feature attributes it constructs a triangulation, and finally iteratively optimizes the vertex color attributes and updates the triangulation. The results of the used techniques are presented. Compared with existing vector representation technique this method provides advantages for various image operations. This method is useful to vectorize the images of fonts, logos, blueprints and maps.

References
  1. W. A. Barrett and A. S. Cheney, "Object-based image editing," ACMTrans. Graph. , vol. 21, no. 3, pp. 777–784, Jul. 2002.
  2. M. Froumentin, F. Labrosse, and P. Willis, "A vector-basedrepresentation for image warping," Comput. Graph. Forum, vol. 19,no. 3, pp. 419–425, Sep. 2000.
  3. S. Swaminarayan and L. Prasad, "Rapid automated polygonal imagedecomposition," in Proc. 35th Appl. Image Pattern Recognit. Workshop,Oct. 2006, p. 28.
  4. G. Lecot and B. Lévy, "ARDECO: Automatic region detection andconversion," in Proc. Eurograph. Symp. Rendering, 2006.
  5. Y. -K. Lai, S. -M. Hu, and R. R. Martin, "Automatic and topologypreservinggradient mesh generation for image vectorization," ACMTrans. Graph. , vol. 28, no. 3, p. 85, Aug. 2009.
  6. S. Swaminarayan and L. Prasad, "Rapid automated polygonal imagedecomposition," in Proc. 35th Appl. Imag. PatternRecognit. Workshop,Oct. 2006, p. 28.
  7. M. Froumentin, F. Labrosse, and P. Willis, "A vector-based representation for image warping," Comput. Graph. Forum, vol. 19,no. 3, pp. 419–425, Sep. 2000.
  8. G. Lecot and B. Lévy, "ARDECO: Automatic region detection andconversion," in Proc. Eurograph. Symp. Rendering, 2006.
  9. T. Xia, B. Liao, and Y. Yu, "Patch-based image vectorization withautomatic curvilinear feature alignment," ACM Trans. Graph. , vol. 28,no. 5, p. 115, Dec. 2009.
  10. D. Hale, "Atomic images-a method for meshing digital images," in Proc. 10th Int. Meshing Roundtable, 2001, pp. 185–196
  11. J. G. Brankov, Y. Yang, and M. N. Wernick, "Tomography imagereconstruction using content-adaptive mesh modeling," in Proc. Int. Conf. Image Process. , 2001, vol. 1, pp. 690–693.
  12. Y. Wang and O. Lee, "Active mesh-a feature seeking and tracking image sequence representation scheme," IEEE Trans. Image Process. , vol. 3,no. 5, pp. 610–624, Sep. 1994.
  13. Y. Altunbasak and A. M. Tekalp, "Closed-form connectivity-preservingsolutions for motion compensation using 2-D meshes," IEEE Trans. Image Process. , vol. 6, no. 9, pp. 1255–1269, Sep. 1997.
  14. S. A. Coleman, B. W. Scotney, and M. G. Herron, "Image featuredetection on content-based meshes," in Proc. Int. Conf. Image Process. ,2002, vol. 1, pp. I-844–I-847.
  15. F. Davoine, M. Antonini, J. -M. Chassery, and M. Barlaud, "Fractal imagecompression based on Delaunay triangulation and vector quantization," IEEE Trans. Image Process. , vol. 5, no. 2, pp. 338–346, Feb. 1996.
  16. Y. Wang, O. Lee, and A. Vetro, "Use of two-dimensional deformablemesh structures for video coding. II. The analysis problem and aregion-based coder employing an active mesh representation," IEEETrans. Circuits Syst. Video Technol. , vol. 6, no. 6, pp. 647–659, Dec. 1996.
  17. D. Su and P. Willis, "Image interpolation by pixel-level data-dependenttriangulation," Comput. Graph. Forum, vol. 23, no. 2, pp. 189–201,Jul. 2004.
  18. B. Price and W. Barrett, "Object-based vectorization for interactive image editing," in Proc. Pacific Graph. , Sep. 2006, pp. 661–670.
  19. Hailing Zhou, Jianmin Zheng, and Lei Wei, "Representing Images Using Curvilinear Feature Driven Subdivision Surfaces" IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 8, AUGUST 2014.
  20. E. Cohen, T. Tyche, and R. Riesenfeld, "Discrete B-splines andsubdivision techniques in computer-aided geometric design andcomputer graphics," Comput. Graph. Image Process. , vol. 14, no. 2,pp. 87–111, Oct. 1980.
  21. M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik, "Using contours todetect and localize junctions in natural images," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1–8.
Index Terms

Computer Science
Information Sciences

Keywords

Image Processing Subdivision Surfaces Curvilinear Features Mesh Generation Graphics Primitives.