An Optimized Image Segmentation Approach Based on Boltzmann Machine

References

• Alam, F. I., M. I. H. Chowdhury, M. R. Rabbani, and F. K. Bappee. 2013. An optimized image segmentation algorithm. Proceedings of the 2013 International Conference on Informatics Electronics and Vision (ICIEV), Dhaka, Bangladesh, 1–6. doi: 10.1109/ICIEV.2013.6572644.
   Google Scholar
• Comaniciu, D., and P. Meer. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24 (5):603–19. doi:10.1109/34.1000236.
   Web of Science ®Google Scholar
• Gulshan, V., C. Rother, A. Criminisi, A. Blake, and A. Zisserman. 2010. Geodesic star convexity for interactive image segmentation. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3129–36, San Francisco, CA. doi: 10.1109/CVPR.2010.5540073.
   Google Scholar
• Gutzmann, K. M. 1987. Combinatorial optimization using a continuous state Boltzmann machine. Proceedings of IEEE First Annual Conf. on Neural Networks III:721–734, San Diego, CA, June 21–24.
   Google Scholar
• Karasulu, B. 2013. An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation. Applied Soft Computing 13 (12):4763–85. doi:10.1016/j.asoc.2013.07.019.
   Web of Science ®Google Scholar
• Karasulu, B., and S. Korukoglu. 2011. A simulated annealing-based optimal threshold determining method in edge-based segmentation of grayscale images. Applied Soft Computing 11 (2):2246–59. doi:10.1016/j.asoc.2010.08.005.
   Web of Science ®Google Scholar
• Kirkpatrick, S., C. Gelatt Jr, and M. Vecchi. 1983. Optimization by simulated annealing. Science 220 (4598):671–80. doi:10.1126/science.220.4598.671.
   PubMed Web of Science ®Google Scholar
• Laganiere, R. 2011. OpenCV 2 computer vision application programming cookbook, 277. Packt Publishing Ltd., Birmingham, United Kingdom. ISBN: 978-1-849513-24-1.
   Google Scholar
• Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. 1953. Equation of state calculation by fast computing machines. Journal of Chemical Physics 21 (6):1087–92. doi:10.1063/1.1699114.
   Web of Science ®Google Scholar
• Peng, R., and P. K. Varshney. 2015. On performance limits of image segmentation algorithms. Computer Vision and Image Understanding 132:24–38. doi:10.1016/j.cviu.2014.11.004.
   Web of Science ®Google Scholar
• Rother, C., V. Kolmogorov, and A. Blake. 2004. Grabcut: Interactive foreground extraction using iterated graph cuts. Proceedings of ACM Transactions on Graphics (TOG) SIGGRAPH 2004. 23(3):309–314, New York, USA, ACM. doi: 10.1145/1015706.1015720.
   Google Scholar
• Shi, J., and C. Tomasi. 1994. Good features to track. Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognit. (CVPR), 593–600. doi:10.1109/CVPR.1994.323794.
   Google Scholar
• Slabaugh, G., and G. Unal. 2005. Graph cuts segmentation using an elliptical shape prior. Proceedings of the IEEE International Conf. on Image Processing 2005:II-1222–1225. doi: 10.1109/ICIP.2005.1530282.
   Google Scholar
• Tesar, B. B., J. Kapenga, and R. Trenary. 1989. A Boltzmann machine solution of the traveling salesperson problem: A study for parallel implementation. Proceedings of the TENCON ‘89 Fourth IEEE Region 10 International Conference, 142–44, Bombay, India, Publ by IEEE (Piscataway, NJ, United States), November 22–24, 1989. doi: 10.1109/TENCON.1989.176914.
   Google Scholar
• Veksler, O. 2008. star shape prior for graph-cut image segmentation. In Computer Vision – ECCV 2008: 10th European conference on computer vision, eds. D. Forsyth, P. Torr, and A. Zisserman, Marseille, France. October 12-18, 2008, Proceedings Part III:454-467. LNCS, vol 5304. Springer, Berlin, Heidelberg. doi:10.1007/978-3-540-88690-7_34.
   Google Scholar
• Wang, J., K. Markert, and M. Everingham. 2009. Learning models for object recognition from natural language descriptions. Proceedings of the 20th British Machine Vision Conference (BMVC2009), 2.1–2.11, BMVA Press. doi: 10.5244/c.23.2.
   Google Scholar
• Wu, J., Y. Zhao, J. Y. Zhu, S. Luo, and Z. Tu. 2014. MILCut: A sweeping line multiple instance learning paradigm for interactive image segmentation. Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, OH, 256–63. doi: 10.1109/CVPR.2014.40.
   Google Scholar