{"title":"Probabilistic Bhattacharya Based Active Contour Model in Structure Tensor Space","authors":"Hiren Mewada, Suprava Patnaik","volume":82,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":1325,"pagesEnd":1330,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/17104","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.<\/p>\r\n","references":"[1] Chan T, Vese L. \u201cActive contours without edges\u201d IEEE Trans Image\r\nProcess Vol.10 (2):266\u2013277, Feb 2001.\r\n[2] S. Lankton and A. Tannenbaum, \u201cLocalizing region based active\r\ncontour\u201d, IEEE Transactions on Image Processing, Vol. 17, No. 11, pp.\r\n2029-2039, 2008.\r\n[3] C. Li., C. Kao, J. Gore and Z. Ding, \u201cImplicit active contour driven by\r\nlocal binary fitting energy\u201d, IEEE Conference on Computer Vision and\r\nPattern Recognition, pp. 1\u20137, 2007.\r\n[4] Haiyong Xu, Tingting Liu and Gautao Wang, \u201cHybrid geodesic region\r\nbased active contours for image segmentation\u201d, Computers & Electrical\r\nEngineering, Available online 26 August 2013, ISSN 0045-7906,\r\nhttp:\/\/dx.doi.org\/10.1016\/j.compeleceng.2013.07.026.\r\n[5] Aubert, G., Barlaud, M., Faugeras, O., & Jehan-Besson, S. \u201cImage\r\nsegmentation using active contours: calculus of variations or shape\r\ngradients\u201d, SIAM Journal on Applied Mathematics, Vol. 1(2), pp. 2128\u2013\r\n2145, 2005.\r\n[6] Ariane Herbulot, St\u00e9phanie Jehan-Besson, Stefan Duffner, Michel\r\nBarlaud, Gilles Aubert. \u201cSegmentation of vectorial image features using\r\nshape gradients and information measures\u201d,journal of Mathematical\r\nImaging and Vision, Vol. 25(3), pp. 365\u2013386, 2006.\r\n[7] Michailovich, O., Rathi, Y., & Tannenbaum, A. \u201cImage segmentation\r\nusing active contours driven by the Bhattacharya gradient flow\u2019, IEEE\r\nTransactions on Image Processing, Vol. 16(11), pp. 2787\u2013 2801, 2007.\r\n[8] Georgiou, T. , Michailovich, O., Rathi, Y, Malcolm, J.,& Tannenbaum,\r\nA. \u201c Distribution metrics and image segmentation\u201d, Linear Algebra and\r\nits Applications,vol. 405, pp. 663\u2013672, 2007.\r\n[9] C. Feddern, J. Weickert and B. Burgeth, \u201cLevel-set methods for tensorvalued\r\nimages\u201d, Proc. Second IEEE Workshop on Variational,\r\nGeometric and Level Set Methods in Computer Vision, pp. 65- 72, 2003.\r\n[10] S. M. Lee, A.L. Abott, N.A. Clark and P. A. Araman, \u201cActive contours\r\non statistical manifolds and texture segmentation\u201d, Proc. IEEE\r\nInternational Conference on Image Processing (ICIP), Vol. 3, pp. 828-\r\n831, 2005. [11] Michael Black, Guilleromo, Sapiro, \u2018Edges as Outliers: Anisotropic\r\nSmoothing using Local Image Statistics\u201d, Scale space theory in\r\nComputer vision, pp. 259-270, Sept 1999.\r\n[12] S. Chen, B. Mulgrew, and P. M. Grant, \u201cA clustering technique for\r\ndigital communications channel equalization using radial basis function\r\nnetworks,\u201d IEEE Trans. Neural Networks, vol. 4, pp. 570\u2013578, July\r\n1993.\r\n[13] Berkeley Image Database http:\/\/www.eecs.berkeley.edu\/Research\/\r\nProjects\/CS\/vision\/bsds\/BSDS300\/html\/dataset\/images.html. Accessed\r\non 18 October 2011.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 82, 2013"}