Page 52 - Fister jr., Iztok, and Andrej Brodnik (eds.). StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference. Koper: University of Primorska Press, 2018
P. 52
] Das, S., & Konar, A. (2009). Automatic image pixel Computer Science Research Conference, University of
clustering with an improved differential evolution. Applied Maribor, Slovenia, pp.5-12, 2017.
Soft Computing, 9(1), 226-236. [35] Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: a
feature similarity index for image quality assessment. IEEE
[20] Omran, M., Engelbrecht, A. P., & Salman, A. (2005). transactions on Image Processing, 20(8), 2378-2386.
Particle swarm optimization method for image clustering.
International Journal of Pattern Recognition and Artificial
Intelligence, 19(03), 297-321.
[21] Xin Ye & Yong-Xian Jin (2016) A Fuzzy C-Means
Algorithm Based on Improved Quantum Genetic
Algorithm,” International Journal of Database Theory and
Application, vol. 9-1, pages 227-236.
[22] Kapoor, S., Zeya, I., Singhal, C., & Nanda, S. J. (2017). A
Grey Wolf Optimizer Based Automatic Clustering Algorithm
for Satellite Image Segmentation. Procedia Computer
Science, 115, 415-422.
[23] Li, H., Zhang, S., Zhang, C., Li, P., & Cropp, R. (2017). A
novel unsupervised Levy flight particle swarm optimization
(ULPSO) method for multispectral remote-sensing image
classification. International Journal of Remote Sensing,
38(23), 6970-6992.
[24] Liang, Y., Zhang, M., & Browne, W. N. (2014, December).
Image segmentation: a survey of methods based on
evolutionary computation. In Asia-Pacific Conference on
Simulated Evolution and Learning (pp. 847-859). Springer,
Cham.
[25] Bong, C. W., & Rajeswari, M. (2012). Multiobjective
clustering with metaheuristic: current trends and methods in
image segmentation. IET image processing, 6(1), 1-10.
[26] De Falco, I., Della Cioppa, A., & Tarantino, E. (2007).
Facing classification problems with particle swarm
optimization. Applied Soft Computing, 7(3), 652-658.
[27] Yang, X. S., & Deb, S. (2009, December). Cuckoo search via
Lévy flights. In Nature & Biologically Inspired Computing,
2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.
[28] Yang, X. S. (2010). Nature-inspired metaheuristic
algorithms, Luniver press.
[29] Gelasca, E. D., Byun, J., Obara, B., & Manjunath, B. S.
(2008, October). Evaluation and benchmark for biological
image segmentation. In Image Processing, 2008. ICIP 2008.
15th IEEE International Conference on (pp. 1816-1819).
IEEE.
[30] Gelasca, E. D., Obara, B., Fedorov, D., Kvilekval, K., &
Manjunath, B. S. (2009). A biosegmentation benchmark for
evaluation of bioimage analysis methods. BMC
bioinformatics, 10(1), 368
[31] Aja-Fernandez, S., Estepar, R. S. J., Alberola-Lopez, C., &
Westin, C. F. (2006, August). Image quality assessment
based on local variance. In Engineering in Medicine and
Biology Society, 2006. EMBS'06. 28th Annual International
Conference of the IEEE (pp. 4815-4818). IEEE.
[32] Dhal, K. G., Sen, M., & Das, S. (2018). Multi-Thresholding
of Histopathological Images Using Fuzzy Entropy and
Parameterless Cuckoo Search. In Critical Developments and
Applications of Swarm Intelligence (pp. 339-356). IGI
Global.
[33] Suresh, S., & Lal, S. (2017). Multilevel thresholding based
on Chaotic Darwinian Particle Swarm Optimization for
segmentation of satellite images. Applied Soft Computing,
55, 503-522.
[34] Dhal, K. G., Fister Jr., I. & S. Das (2017). Parameterless
Harmony Search for image Multi-thresholding. 4th Student
StuCoSReC Proceedings of the 2018 5th Student Computer Science Research Conference 54
Ljubljana, Slovenia, 9 October
clustering with an improved differential evolution. Applied Maribor, Slovenia, pp.5-12, 2017.
Soft Computing, 9(1), 226-236. [35] Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: a
feature similarity index for image quality assessment. IEEE
[20] Omran, M., Engelbrecht, A. P., & Salman, A. (2005). transactions on Image Processing, 20(8), 2378-2386.
Particle swarm optimization method for image clustering.
International Journal of Pattern Recognition and Artificial
Intelligence, 19(03), 297-321.
[21] Xin Ye & Yong-Xian Jin (2016) A Fuzzy C-Means
Algorithm Based on Improved Quantum Genetic
Algorithm,” International Journal of Database Theory and
Application, vol. 9-1, pages 227-236.
[22] Kapoor, S., Zeya, I., Singhal, C., & Nanda, S. J. (2017). A
Grey Wolf Optimizer Based Automatic Clustering Algorithm
for Satellite Image Segmentation. Procedia Computer
Science, 115, 415-422.
[23] Li, H., Zhang, S., Zhang, C., Li, P., & Cropp, R. (2017). A
novel unsupervised Levy flight particle swarm optimization
(ULPSO) method for multispectral remote-sensing image
classification. International Journal of Remote Sensing,
38(23), 6970-6992.
[24] Liang, Y., Zhang, M., & Browne, W. N. (2014, December).
Image segmentation: a survey of methods based on
evolutionary computation. In Asia-Pacific Conference on
Simulated Evolution and Learning (pp. 847-859). Springer,
Cham.
[25] Bong, C. W., & Rajeswari, M. (2012). Multiobjective
clustering with metaheuristic: current trends and methods in
image segmentation. IET image processing, 6(1), 1-10.
[26] De Falco, I., Della Cioppa, A., & Tarantino, E. (2007).
Facing classification problems with particle swarm
optimization. Applied Soft Computing, 7(3), 652-658.
[27] Yang, X. S., & Deb, S. (2009, December). Cuckoo search via
Lévy flights. In Nature & Biologically Inspired Computing,
2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.
[28] Yang, X. S. (2010). Nature-inspired metaheuristic
algorithms, Luniver press.
[29] Gelasca, E. D., Byun, J., Obara, B., & Manjunath, B. S.
(2008, October). Evaluation and benchmark for biological
image segmentation. In Image Processing, 2008. ICIP 2008.
15th IEEE International Conference on (pp. 1816-1819).
IEEE.
[30] Gelasca, E. D., Obara, B., Fedorov, D., Kvilekval, K., &
Manjunath, B. S. (2009). A biosegmentation benchmark for
evaluation of bioimage analysis methods. BMC
bioinformatics, 10(1), 368
[31] Aja-Fernandez, S., Estepar, R. S. J., Alberola-Lopez, C., &
Westin, C. F. (2006, August). Image quality assessment
based on local variance. In Engineering in Medicine and
Biology Society, 2006. EMBS'06. 28th Annual International
Conference of the IEEE (pp. 4815-4818). IEEE.
[32] Dhal, K. G., Sen, M., & Das, S. (2018). Multi-Thresholding
of Histopathological Images Using Fuzzy Entropy and
Parameterless Cuckoo Search. In Critical Developments and
Applications of Swarm Intelligence (pp. 339-356). IGI
Global.
[33] Suresh, S., & Lal, S. (2017). Multilevel thresholding based
on Chaotic Darwinian Particle Swarm Optimization for
segmentation of satellite images. Applied Soft Computing,
55, 503-522.
[34] Dhal, K. G., Fister Jr., I. & S. Das (2017). Parameterless
Harmony Search for image Multi-thresholding. 4th Student
StuCoSReC Proceedings of the 2018 5th Student Computer Science Research Conference 54
Ljubljana, Slovenia, 9 October
