Probabilistic Bhattacharya Based Active Contour Model in Structure Tensor Space
Object identification and segmentation application requires extraction of object in foreground from the background. In this paper the Bhattacharya distance based probabilistic approach is utilized with an active contour model (ACM) to segment an object from the background. In the proposed approach, the Bhattacharya histogram is calculated on non-linear structure tensor space. Based on the histogram, new formulation of active contour model is proposed to segment images. The results are tested on both color and gray images from the Berkeley image database. The experimental results show that the proposed model is applicable to both color and gray images as well as both texture images and natural images. Again in comparing to the Bhattacharya based ACM in ICA space, the proposed model is able to segment multiple object too.
[1] Chan T, Vese L. “Active contours without edges” IEEE Trans Image
Process Vol.10 (2):266–277, Feb 2001.
[2] S. Lankton and A. Tannenbaum, “Localizing region based active
contour”, IEEE Transactions on Image Processing, Vol. 17, No. 11, pp.
2029-2039, 2008.
[3] C. Li., C. Kao, J. Gore and Z. Ding, “Implicit active contour driven by
local binary fitting energy”, IEEE Conference on Computer Vision and
Pattern Recognition, pp. 1–7, 2007.
[4] Haiyong Xu, Tingting Liu and Gautao Wang, “Hybrid geodesic region
based active contours for image segmentation”, Computers & Electrical
Engineering, Available online 26 August 2013, ISSN 0045-7906,
http://dx.doi.org/10.1016/j.compeleceng.2013.07.026.
[5] Aubert, G., Barlaud, M., Faugeras, O., & Jehan-Besson, S. “Image
segmentation using active contours: calculus of variations or shape
gradients”, SIAM Journal on Applied Mathematics, Vol. 1(2), pp. 2128–
2145, 2005.
[6] Ariane Herbulot, Stéphanie Jehan-Besson, Stefan Duffner, Michel
Barlaud, Gilles Aubert. “Segmentation of vectorial image features using
shape gradients and information measures”,journal of Mathematical
Imaging and Vision, Vol. 25(3), pp. 365–386, 2006.
[7] Michailovich, O., Rathi, Y., & Tannenbaum, A. “Image segmentation
using active contours driven by the Bhattacharya gradient flow’, IEEE
Transactions on Image Processing, Vol. 16(11), pp. 2787– 2801, 2007.
[8] Georgiou, T. , Michailovich, O., Rathi, Y, Malcolm, J.,& Tannenbaum,
A. “ Distribution metrics and image segmentation”, Linear Algebra and
its Applications,vol. 405, pp. 663–672, 2007.
[9] C. Feddern, J. Weickert and B. Burgeth, “Level-set methods for tensorvalued
images”, Proc. Second IEEE Workshop on Variational,
Geometric and Level Set Methods in Computer Vision, pp. 65- 72, 2003.
[10] S. M. Lee, A.L. Abott, N.A. Clark and P. A. Araman, “Active contours
on statistical manifolds and texture segmentation”, Proc. IEEE
International Conference on Image Processing (ICIP), Vol. 3, pp. 828-
831, 2005. [11] Michael Black, Guilleromo, Sapiro, ‘Edges as Outliers: Anisotropic
Smoothing using Local Image Statistics”, Scale space theory in
Computer vision, pp. 259-270, Sept 1999.
[12] S. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for
digital communications channel equalization using radial basis function
networks,” IEEE Trans. Neural Networks, vol. 4, pp. 570–578, July
1993.
[13] Berkeley Image Database http://www.eecs.berkeley.edu/Research/
Projects/CS/vision/bsds/BSDS300/html/dataset/images.html. Accessed
on 18 October 2011.
[1] Chan T, Vese L. “Active contours without edges” IEEE Trans Image
Process Vol.10 (2):266–277, Feb 2001.
[2] S. Lankton and A. Tannenbaum, “Localizing region based active
contour”, IEEE Transactions on Image Processing, Vol. 17, No. 11, pp.
2029-2039, 2008.
[3] C. Li., C. Kao, J. Gore and Z. Ding, “Implicit active contour driven by
local binary fitting energy”, IEEE Conference on Computer Vision and
Pattern Recognition, pp. 1–7, 2007.
[4] Haiyong Xu, Tingting Liu and Gautao Wang, “Hybrid geodesic region
based active contours for image segmentation”, Computers & Electrical
Engineering, Available online 26 August 2013, ISSN 0045-7906,
http://dx.doi.org/10.1016/j.compeleceng.2013.07.026.
[5] Aubert, G., Barlaud, M., Faugeras, O., & Jehan-Besson, S. “Image
segmentation using active contours: calculus of variations or shape
gradients”, SIAM Journal on Applied Mathematics, Vol. 1(2), pp. 2128–
2145, 2005.
[6] Ariane Herbulot, Stéphanie Jehan-Besson, Stefan Duffner, Michel
Barlaud, Gilles Aubert. “Segmentation of vectorial image features using
shape gradients and information measures”,journal of Mathematical
Imaging and Vision, Vol. 25(3), pp. 365–386, 2006.
[7] Michailovich, O., Rathi, Y., & Tannenbaum, A. “Image segmentation
using active contours driven by the Bhattacharya gradient flow’, IEEE
Transactions on Image Processing, Vol. 16(11), pp. 2787– 2801, 2007.
[8] Georgiou, T. , Michailovich, O., Rathi, Y, Malcolm, J.,& Tannenbaum,
A. “ Distribution metrics and image segmentation”, Linear Algebra and
its Applications,vol. 405, pp. 663–672, 2007.
[9] C. Feddern, J. Weickert and B. Burgeth, “Level-set methods for tensorvalued
images”, Proc. Second IEEE Workshop on Variational,
Geometric and Level Set Methods in Computer Vision, pp. 65- 72, 2003.
[10] S. M. Lee, A.L. Abott, N.A. Clark and P. A. Araman, “Active contours
on statistical manifolds and texture segmentation”, Proc. IEEE
International Conference on Image Processing (ICIP), Vol. 3, pp. 828-
831, 2005. [11] Michael Black, Guilleromo, Sapiro, ‘Edges as Outliers: Anisotropic
Smoothing using Local Image Statistics”, Scale space theory in
Computer vision, pp. 259-270, Sept 1999.
[12] S. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for
digital communications channel equalization using radial basis function
networks,” IEEE Trans. Neural Networks, vol. 4, pp. 570–578, July
1993.
[13] Berkeley Image Database http://www.eecs.berkeley.edu/Research/
Projects/CS/vision/bsds/BSDS300/html/dataset/images.html. Accessed
on 18 October 2011.
@article{"International Journal of Electrical, Electronic and Communication Sciences:65417", author = "Hiren Mewada and Suprava Patnaik", title = "Probabilistic Bhattacharya Based Active Contour Model in Structure Tensor Space", abstract = "Object identification and segmentation application requires extraction of object in foreground from the background. In this paper the Bhattacharya distance based probabilistic approach is utilized with an active contour model (ACM) to segment an object from the background. In the proposed approach, the Bhattacharya histogram is calculated on non-linear structure tensor space. Based on the histogram, new formulation of active contour model is proposed to segment images. The results are tested on both color and gray images from the Berkeley image database. The experimental results show that the proposed model is applicable to both color and gray images as well as both texture images and natural images. Again in comparing to the Bhattacharya based ACM in ICA space, the proposed model is able to segment multiple object too.
", keywords = "Active Contour, Bhattacharya Histogram, Structure tensor, Image segmentation. ", volume = "7", number = "10", pages = "1345-5", }
