M. Celenk, “A color clustering technique for image segmentation”, Computer Vision, Graphics, and Image Processing, Vol. 52, No. 2, 1990, pp. 145-170.10.1016/0734-189X(90)90052-W
A. Tremeau, N. Borel, “A region growing and merging algorithm to color segmentation”, Pattern Recognition, Vol. 30, No. 7, 1997, pp. 1191-1203.10.1016/S0031-3203(96)00147-1
H. D. Cheng, Y. Sun, “A hierarchical approach to color image segmentation using homogeneity”, IEEE Trans. on Image Processing, Vol. 9, No. 12, 2000, pp. 2071-2082.10.1109/83.887975
O. Lezoray, H. Cardot, “Cooperation of color pixel classification schemes and color watershed: a study for microscopic images”, IEEE Transactions on Image Processing, Vol. 11, No. 7, 2002, pp. 783-789.10.1109/TIP.2002.800889
L. Shafarenko, M. Petrou, J. Kittler, “Automatic watershed segmentation of randomly textured color images”, IEEE Trans. on Image Processing, Vol. 6, No. 11, 1997, pp. 1530-1544.10.1109/83.641413
A. Shiji, N. Hamada, “Color image segmentation method using watershed algorithm and contour information”, International Conference on Image Processing, 1999, pp. 305-309.
L. H. Ma, Y. Zhang, J. P. Deng, “A target segmentation algorithm based on opening-closing binary marker on watersheds and texture merging”, Journal of Image and Graphics, Vol. 8, 2003, No 1, pp. 77-83.
G. Celeux, F. Forbes, N. Peyrard, “EM procedures using mean field-like approximations for Markov model-based image segmentation”, Pattern Recognition, Vol. 36, No. 1, 2003, pp.131144.10.1016/S0031-3203(02)00027-4
Y. Zhang, M. Brady, S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm”, IEEE Transactions on Medical Imaging, Vol. 20, No. 1, 2001, pp. 45-57.10.1109/42.906424
C. L. Huang, T. Y. Cheng, C. C. Chen, “Color images segmentation using scale space filter and Markov random field”, Pattern Recognition, Vol. 25, No. 10, 1992, pp. 1217-1229.10.1016/0031-3203(92)90023-C
F. Kurugollu, B. Sankur, “Color image segmentation using histogram multi-thresholding and fusion”, Image and Vision Computing, Vol. 19, No. 13, 2001, pp. 915-928.10.1016/S0262-8856(01)00052-X
C. Q. Liu, H. Cheng, “An efficient clustering method for color image segmentation”, Pattern Recognition and Artificial Intelligence, Vol. 8, No. A01, 1995, pp. 133-138.
D. Carevic, T. Caelli, “Region-Based Coding of Color Images Using Karhunen-Loeve Transform”, Computer Vision, Graphics, and Image Processing, Vol. 59, No. 1, 1997, pp. 27-38.10.1006/gmip.1996.0402
X. W. Wang, L. S. Shen, B. G. Wei, “The focus segmentation of color ophthalmologic image based on modified K-means clustering and mathematical morphology”, Chinese Journal of Biomedical Engineering, Vol. 5, 2002.
N. Zahid, M. Limouri, A. Esseaid, “A new cluster-validity for fuzzy clustering”, Pattern Recognition, Vol. 32, No. 7, 1999, pp. 1089-1097.10.1016/S0031-3203(98)00157-5
M. Sammouda, R. Sammouda, N. Niki, “Segmentation and analysis of liver cancer pathological color image based on artificial neural networks”, ICIP’99, Vol. 3, 1999, pp. 392-396.
M. Sammouda, R. Sammouda, “Improving the performance of Hopfield neural network to segment pathological liver color images”, Proceedings of the 17th International Congress and Exhibition, London, June 25-28, 2003.10.1016/S0531-5131(03)00394-7
S. H. Ong, N. C. Yeo, K. H. Lee, “Segmentation of color images using a two-stage selforganizing network”, Image and Vision Computing, Vol. 4, 2002.10.1016/S0262-8856(02)00021-5
N. Papamarkos, C. Strouthopoulous, and L. Andreadis, “Multithresholding of color and gray-level images through a neural network technique”, Image and Vision Computing, Vol. 18, No. 3, 2000, pp. 213-222.10.1016/S0262-8856(99)00015-3
N. K. Suryadevara and S. C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.10.1109/JSEN.2011.2182341
N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.10.1109/MIS.2014.16
Rui Xu and D. Wunsch, “Survey of Clustering Algorithms”, IEEE Transaction on Neural Networks, Vol. 16, No. 3, 2005, pp. 645-678.10.1109/TNN.2005.84514115940994
M. Filippone, F. Camastra, and F. Masulli, “A Survey of Kernel and Spectral Methods for Clustering”, Pattern Recognition, Vol. 41, No. 1, 2008, pp. 176-190.10.1016/j.patcog.2007.05.018
C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167.10.1023/A:1009715923555
D. M. J. Tax, R. P. W. Duin, “Support Vector Domain Description”, Pattern Recognition Letters, Vol. 20, No. 11-13, 1999, pp. 1191-1199.10.1016/S0167-8655(99)00087-2
A. Ben-Hur, D. Horn, H. T. Siegelmann, V. Vapnik, “Support Vector Clustering”, Journal of Machine Learning Research, Vol. 2, No. 12, 2001, pp. 125-137.
B. Scholkopf, R. Williamson, A. Smola, J. Shawe-Taylor, J. Platt, “Support Vector Method for Novelty Detection”, Advances in Neural Information Processing System, Vol. 12, 2000, pp. 582-588.
N. K. Suryadevara, S. C. Mukhopadhyay, R. Wang, R. K. Rayudu, “Forecasting the behavior of an elderly using wireless sensors data in a smart home”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 10, 2013, pp. 2641-2652.10.1016/j.engappai.2013.08.004
G. Sen Gupta, S. C. Mukhopadhyay and M. Finnie, “Wi-Fi Based Control of a Robotic Arm with Remote Vision”, Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 5-7, 2009, pp. 557-562.10.1109/IMTC.2009.5168512
Yongqing Wang, “NEW INTELLIGENT CLASSIFICATION METHOD BASED ON IMPROVED MEB ALGORITHM”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 1, 2014, pp. 72-95.10.21307/ijssis-2017-646
Yongqing Wang, “FACE RECOGNITION BASED ON IMPROVED SUPPORT VECTOR CLUSTERING”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 4, 2014, pp. 1807-1829.10.21307/ijssis-2017-734