CFP last date
20 May 2024
Reseach Article

Object Segmentation and Classification using Multiple Shape Models from an image sequence

Published on None 2011 by P.Swathika, S.Vanitha Sivagami
journal_cover_thumbnail
International Conference on VLSI, Communication & Instrumentation
Foundation of Computer Science USA
ICVCI - Number 11
None 2011
Authors: P.Swathika, S.Vanitha Sivagami
0d4e7d23-57a7-45f0-9f51-ba941c7ab3aa

P.Swathika, S.Vanitha Sivagami . Object Segmentation and Classification using Multiple Shape Models from an image sequence. International Conference on VLSI, Communication & Instrumentation. ICVCI, 11 (None 2011), 24-29.

@article{
author = { P.Swathika, S.Vanitha Sivagami },
title = { Object Segmentation and Classification using Multiple Shape Models from an image sequence },
journal = { International Conference on VLSI, Communication & Instrumentation },
issue_date = { None 2011 },
volume = { ICVCI },
number = { 11 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 24-29 },
numpages = 6,
url = { /proceedings/icvci/number11/2711-1449/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on VLSI, Communication & Instrumentation
%A P.Swathika
%A S.Vanitha Sivagami
%T Object Segmentation and Classification using Multiple Shape Models from an image sequence
%J International Conference on VLSI, Communication & Instrumentation
%@ 0975-8887
%V ICVCI
%N 11
%P 24-29
%D 2011
%I International Journal of Computer Applications
Abstract

A method of segmenting, classifying the multiple shapes from an image sequence is presented. This paper presents the segmentation framework that allows multiple shapes to be segmented simultaneously in a seamless fashion. Shape models (SMs), contains features of a set of training shapes, represent a new object present in the image. It is based on clustering a set of training shapes in the original shape space (defined by the coordinates of the contour points) based on the similarity measure. This method uses the prior shape and the prior shape information. Two main goals in this paper are segmenting the image and classifying the image.

References
  1. M. E. Leventon, W. E. L. Grimson, and O. D. Faugeras, ―Statistical Shape influence in geodesic active contours, in Proc. CVPR, 2000, vol., pp. 1316–1323.
  2. G. Charpiat, O. D. Faugeras, and R.Keriven, ―Shape statistics for imagesegmentation with prior, in Proc. CVPR, 2007, pp. 1–6.
  3. S. Dambreville, Y. Rathi, and A. Tannenbaum, ―Shape-based approach to robust image segmentation using Kernel PCA, in Proc. CVPR, 2006, vol. 1, pp. 977–984.
  4. T. F. Chan and L. A. Vese, ―Active contours without edges, IEEE Trans. Image Process., vol. 10, pp. 266–277, 2001.
  5. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, ―Active shape models— Their training and application, Comput. Vis. Image Understand. vol. 61, no. 1, pp. 38–59, 1995.
  6. R. R. Coifman and S. Lafon, ―Diffusion maps, Appl. Comput. Harmon. Anal., vol. 21, no. 1, pp. 5–30, 2006.
  7. G. Sapiro, R. Kimmel, and V. Caselles, ―Geodesic active contours, in Proc. ICCV, 1995, pp. 694–699.
  8. A. Srivastava and I. H. Jermyn, ―Looking for shapes in two- dimensional cluttered point clouds, IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 9, pp. 1616–1629, Sep. 2009.
  9. N. Vu and B. S. Manjunath, ―Shape prior segmentation of multiple objects with graph cuts, in Proc. CVPR, 2008, pp. 1–8.
  10. L. A.Vese and T. F. Chan, ―A multiphase level set framework for image segmentation using the mumford and shah model, Int. J. Comput. Vis., vol. 50, no. 3, pp. 271–293, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Scalable Encryption Algorithm Shape Models Prior Activation