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Reseach Article

Object Segmentation based on Shape Feature using Active Contour Model

by Snehal V. Talikoti, J .V. Shinde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 10
Year of Publication: 2016
Authors: Snehal V. Talikoti, J .V. Shinde
10.5120/ijca2016910930

Snehal V. Talikoti, J .V. Shinde . Object Segmentation based on Shape Feature using Active Contour Model. International Journal of Computer Applications. 146, 10 ( Jul 2016), 15-19. DOI=10.5120/ijca2016910930

@article{ 10.5120/ijca2016910930,
author = { Snehal V. Talikoti, J .V. Shinde },
title = { Object Segmentation based on Shape Feature using Active Contour Model },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 10 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number10/25434-2016910930/ },
doi = { 10.5120/ijca2016910930 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:04.107838+05:30
%A Snehal V. Talikoti
%A J .V. Shinde
%T Object Segmentation based on Shape Feature using Active Contour Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 10
%P 15-19
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Active contour models has already under study from the past many years, several methods have been proposed for forming contours. Active contour are the curves that are generated by the computer that move within an images to find boundaries of the object in an image. They are also used in image analysis and computer vision to recognize and find objects, and to describe their shape. In the proposed system Active Contour Model that segments one or many regions of the image that are visually alike to an object of interest said as prior. The probability density function is used for extracting the color feature by applying heuristic rule, and then the new proposed shape detection algorithm is used to detect the photometric feature shape of the object in an image. For accurately segmenting object in an image. The proposed system provides the accurate results on real world and synthetic datasets.

References
  1. Michela Lecca, Stefano Messelodi, Raul Paolo serapioni. “A New Region Based Active Contour Model for Object Segmentation” Springer, J Math Imaging Vis 2015
  2. Kass, M.,Witkin, A.,Terzopoulos,D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
  3. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
  4. Chakraborty, A., Staib, L., Duncan, J.: Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans. Med. Image 15(6), 859–870 (1996)
  5. Darolti, C., Mertins, A., Bodensteiner, C., Hofmann, U.G.: Local region descriptors for active contours evolution. Trans. Image Proc. 17(12), 2275–2288 (2008)
  6. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vis. 46(3),223247(2002)
  7. Ronfard, R.: Region-based strategies for active contour models. Int. J. Comput. Vis. 13, 229251 (1994)
  8. Jehan-Besson, S., Barlaud, M., Aubert, G., Faugeras, O.: Shape gradients for histogram segmentation using active contours. In: Proceedings of International Conference on Computer Vision, pp.
  9. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266277 (2001)
  10. Darolti, C., Mertins, A., Bodensteiner, C., Hofmann, U.G.:Local region descriptors for active contours evolution. Trans. Image Proc. 17(12), 22752288 (2008)
  11. Atkinson, K., Han, W.: Theoretical Numerical Analysis. A Functional Analysis Framework, Texts in Applied Mathematics, vol. 39. Springer, New York (2009)
  12. Lecca, M.,Messelodi, S.: Rotation, rescaling and occlusion invariant object retrieval. In: Rajpoot, N., Bhalerao, A. (eds.) British machine vision conferenceBMVC, BMVAPress, pp. 14.1-14.10 (2007)
  13. FBK-TeV: Technologies of vision—Fondazione Bruno Kessler, masks of coil-100 dabatabase. https://tev-static.fbk.eu/DATA BASES/coil-100-masks.tgz (2006)
  14. He, L., Peng, Z., Everding, B., Wang, X., Han, C.Y., Weiss, K.L., Wee, W.G.: A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 26(2), 141–163 (2008)
Index Terms

Computer Science
Information Sciences

Keywords

Active contour model Probability density function and heuristic rules.