A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model

In this paper, a novel approach to texture segmentation based on the parametric active contour model (ACM) is proposed. At first, gray-level co-occurrence matrix and subsequently co-occurrence energy of the regions inside and outside of the dynamic contour are calculated. Difference of this energy corresponding to both the regions is used as the external energy of the proposed ACM. The contour stops and converges completely when this difference attains a maximum value. The proposed approach requires only initial contour selection and no object point selection like the other variants of parametric ACM used for texture segmentation. Experiments on a number of synthetic and real-world texture images show that in all cases, we are getting a better segmentation of the object although for few cases the execution time is bit more than that of other existing methods.

[1]  David A. Clausi,et al.  Hybrid structural and texture distinctiveness vector field convolution for region segmentation , 2014, Comput. Vis. Image Underst..

[2]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[3]  Subhransu Maji,et al.  Deep filter banks for texture recognition and segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Priyambada Subudhi,et al.  A pyramidal approach to active contours implementation for 2D gray scale image segmentation , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[5]  Lei Wei,et al.  Texture aware image segmentation using graph cuts and active contours , 2013, Pattern Recognit..

[6]  Patricio A. Vela,et al.  Interactive Medical Image Segmentation using PDE Control of Active Contours , 2013, IEEE Transactions on Medical Imaging.

[7]  Kamal Jamshidi,et al.  Fast texture energy based image segmentation using Directional Walsh-Hadamard Transform and parametric active contour models , 2011, Expert Syst. Appl..

[8]  Yong Gan,et al.  An active contour model based on fused texture features for image segmentation , 2015, Neurocomputing.

[9]  S. Amirhassan Monadjemi,et al.  Parametric active contour model using Gabor balloon energy for texture segmentation , 2016, Signal Image Video Process..

[10]  Bing Li,et al.  Active Contour External Force Using Vector Field Convolution for Image Segmentation , 2007, IEEE Transactions on Image Processing.

[11]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[12]  Priyambada Subudhi,et al.  A fast texture segmentation scheme based on active contours and discrete cosine transform , 2017, Comput. Electr. Eng..

[13]  Yehoshua Y. Zeevi,et al.  Integrated active contours for texture segmentation , 2006, IEEE Transactions on Image Processing.

[14]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[15]  Cheng-Han Tsai,et al.  Rectification-conducted adaptive snake for segmenting complex-boundary objects from textured backgrounds , 2016, Signal Image Video Process..

[16]  Xian Sun,et al.  Interactive geospatial object extraction in high resolution remote sensing images using shape-based global minimization active contour model , 2013, Pattern Recognit. Lett..

[17]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[18]  Payman Moallem,et al.  Texture-based parametric active contour for target detection and tracking , 2009 .

[19]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[20]  Xavier Bresson,et al.  Fast texture segmentation model based on the shape operator and active contour , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Aditya Tatu,et al.  A Novel Active Contour Model for Texture Segmentation , 2013, EMMCVPR.