Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.87MB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Local Phase Oriented Structure Tensor To Segment Texture Images With Intensity Inhomogeneity
Hiren K Mewada, Suprava Patnaik
Pages - 302 - 313     |    Revised - 15-05-2013     |    Published - 30-06-2013
Volume - 7   Issue - 3    |    Publication Date - June 2013  Table of Contents
MORE INFORMATION
KEYWORDS
Linear Structure Tensor, Quadrature filter, Active contour, Image Segmentation
ABSTRACT
This paper proposed the active contour based texture image segmentation scheme using the linear structure tensor and tensor oriented steerable Quadrature filter. Linear Structure tensor (LST) is a popular method for the unsupervised texture image segmentation where LST contains only horizontal and vertical orientation information but lake in other orientation information and also in the image intensity information on which active contour is dependent. Therefore in this paper, LST is modified by adding intensity information from tensor oriented structure tensor to enhance the orientation information. In the proposed model, these phases oriented features are utilized as an external force in the region based active contour model (ACM) to segment the texture images having intensity inhomogeneity and noisy images. To validate the results of the proposed model, quantitative analysis is also shown in terms of accuracy using a Berkeley image database.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 J. Big Aijn, G. H. Granlund, and J. Wiklund. “Multidimensional orientation estimation with applications to texture analysis and optical flow” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13(8), pp.775 -790, 1991.
2 T. Brox. “From pixels to regions: Partial differential equations in image analysis”, PhD Thesis, Mathematical Image Analysis Group, Department of Mathematics and Computer Science Saarland University, Germany, 2005.
3 T. Brox, J. Weickert, B. Burgeth, and P. MrAazek. “Nonlinear structure tensors. Image and Vision Computing”, Vol.24(1), pp.41-55, 2006.
4 R. Estepar. “Local structure tensor for multidimensional signal processing: Application to medical image analysis”, Ph D Thesis, universitaires de Louvain, 2007.
5 C. Feddern, J. Weickert, and B. Burgeth. “Level-set methods for tensor valued images”,Proc. Second IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision,pp. 65-72, 2003.
6 G. H. Granlund. “In search of a general picture processing operator”. Computer Graphics and Image Processing, Vol.8(2), pp.155-173, 1978.
7 H. Knutsson and M. Andersson. “Loglets:Generalized Quadrature and phase for local spatio-temporal structure estimation”. 13th Scandinavian Conference, SCIA-2003 Halmstad, Sweden, July 2003, pp.741 -748.
8 C. Li, C. Kao, J. Gore, and Z. Ding. “Implicit active contour driven by local binary fitting energy”. IEEE Conf on Computer Vision and Pattern Recognition,2007, pp. 1-7.
9 S. Li, J. T. Kwok, H. Zhu, and Y. Wang. Texture classification using the support vector machines. Pattern Recognition, Vol.36(12), pp.2883 - 2893, 2003.
10 A. Lorette, X. Descombes, and J. Zerubia. “Texture analysis through a markovian modelling and fuzzy classification: application to urban area extraction from satellite images”. International Journal of Computer Vision, Vol. 36(5), pp. 221-236, 2002.
11 H. Lu, Y. Liu, Z. Sun, and Y. Chen. “An active contours method based on intensity and reduced Gabor features for texture segmentation”. Intelligent Control and Information Processing (ICICIP),pp. 1369 -137, Nov 2009.
12 O. Michailovich, Y. Rathi and Tannenbaum. “Image segmentation using active contours driven by the Bhattacharyya gradient flow”. IEEE Transactions On Image Processing,Vol.16(11), pp.2787 -2801, 2007.
13 J. Ning, L. Zhang, D. Zhang, and C. Wu. “Interactive image segmentation by maximal similarity based region merging”. Journal of Pattern Recognition, Vol.43(11), pp. 445-456,2010.
14 S. Osher and J. Sethian. “Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations”. Journal of Computational Physics,Vol. 79, pp. 12-49, 1988.
15 B. Sandberg, T. Chan, and L. Vese. “A level-set and Gabor-based active contour algorithm for segmenting textured images”. Technical Report 39, Mathematical Department, UCLA,Los Angeles, 2002.
16 T.Chan and L. Vese. “Active contour without edges”. IEEE Transactions on Image Processing, Vol.10(2), pp. 266 -277, 2001.
17 Y. Wang, Y. Xiong, L. Lv, H. Zhang, Z. Cao, and D. Zhang. “Vector-valued chan-vese model driven by local histogram for texture segmentation”. 17th IEEE International Conference on Image Processing (ICIP), , Sept 2010,pp.645 -648.
18 D. Yang, T. Deng, C. Yang, and J. Bian. “Interactive graph cut method based on improved Gabor features for image segmentation”. Intelligent Control and Information Processing(ICICIP), Vol.1(2), pp.267 - 270, July 2011.
19 Kangyu Ni, Xavier Bresson, Tony Chan and Selim Esedoglu, “Local histogram based segmentation using the Wasserstein distance”, Internation Journal of Computer Vision,Vol. 84, pp. 97-111, April 2009.
20 C.C. Reyes-Aldasoroa,A. Bhalerao, “The Bhattacharyya space for feature selection and its application to texture segmentation” Internation Jounral of Pattern Recognition Vol .39 pp.812 – 826, 2006.
Associate Professor Hiren K Mewada
Charotar University of Science and Technology - India
mewadahiren@gmail.com
Dr. Suprava Patnaik
Xavier Institute of Technology - India