A Review on Image Segmentation Technique
Keywords:
Image Segmentation, Image AnalysiS, Segment, VideoAbstract
Making a segment of an image or any object is what segmentation is all about. The first stages in image segmentation are pattern recognition and picture analysis. We may conduct significant research topics in the segmentation of video with dynamic backdrop in the computer vision area and image analysis. In image processing and analysis, picture segmentation is the most common judging or analyzing function. Picture segmentation is the division of an image into distinct areas that are same or similar in some aspects and homogeneous in others. The outcomes of picture segmentation have an impact on image greater tasks and evaluation Characterization and depiction of objects, as well as feature measurement, are all part of image analysis. The categorization of objects is followed by a higher-order job. As a result, image segmentation relies heavily on characterization, visualization of the part of attention in any image, and demarcation. The current methods of picture segmentation are examined using various algorithms in order to allow user interaction with images. The review of picture segmentation is presented in this article utilizing several methods.
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References
J. Sahoo, S. Mohapatra, and R. Lath, “Virtualization: A survey on concepts, taxonomy and associated security issues,” 2010, doi: 10.1109/ICCNT.2010.49.
B. K. Sahu, S. Pati, P. K. Mohanty, and S. Panda, “Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system,” Appl. Soft Comput. J., 2015, doi: 10.1016/j.asoc.2014.11.027.
M. Waseem Khan, “A Survey: Image Segmentation Techniques,” Int. J. Futur. Comput. Commun., 2014, doi: 10.7763/ijfcc.2014.v3.274.
D. Kaur and Y. Kaur, “Various Image Segmentation Techniques: A Review,” Int. J. Comput. Sci. Mob. Comput., 2014.
N. M. Zaitoun and M. J. Aqel, “Survey on Image Segmentation Techniques,” 2015, doi: 10.1016/j.procs.2015.09.027.
G. Das, P. K. Pattnaik, and S. K. Padhy, “Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization,” Expert Syst. Appl., 2014, doi: 10.1016/j.eswa.2013.10.053.
M. Biswal and P. K. Dash, “Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier,” Digit. Signal Process. A Rev. J., 2013, doi: 10.1016/j.dsp.2013.02.012.
R. P. Mohanty and A. Prakash, “Green supply chain management practices in India: An empirical study,” Prod. Plan. Control, 2014, doi: 10.1080/09537287.2013.832822.
P. Mohapatra, S. Chakravarty, and P. K. Dash, “An improved cuckoo search based extreme learning
machine for medical data classification,” Swarm Evol. Comput., 2015, doi: 10.1016/j.swevo.2015.05.003. [10]G. M. S. Ahmed, A. Algahtani, E. R. I. Mahmoud, and I. A. Badruddin, “Experimental evaluation of interfacial surface cracks in frictionwelded dissimilar metals through image segmentation technique (IST),” Materials (Basel)., 2018, doi: 10.3390/ma11122460. [11]S. Saini and K. Arora, “A Study Analysis on the Different Image Segmentation Techniques,” Int. J. Inf. Comput. Technol., 2014.
S. Panda, S. C. Swain, P. K. Rautray, R. K. Malik, and G. Panda, “Design and analysis of SSSC-based supplementary damping controller,” Simul. Model. Pract. Theory, 2010, doi: 10.1016/j.simpat.2010.04.007.
R. Bisoi and P. K. Dash, “A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter,” Appl. Soft Comput. J., 2014, doi: 10.1016/j.asoc.2014.01.039.
S. Dhar, R. K. Patnaik, and P. K. Dash, “Fault Detection and Location of Photovoltaic Based DC Microgrid Using Differential Protection Strategy,” IEEE Trans. Smart Grid, 2018, doi: 10.1109/TSG.2017.2654267.
A. Sarangi, R. K. Mahapatra, and S. P. Panigrahi, “DEPSO and PSO-QI in digital filter design,” Expert Syst. Appl., 2011, doi: 10.1016/j.eswa.2011.02.140.
M. Rout, B. Majhi, R. Majhi, and G. Panda, “Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training,” J. King Saud Univ. - Comput. Inf. Sci., 2014, doi: 10.1016/j.jksuci.2013.01.002.
M. M. Bhatti, S. R. Mishra, T. Abbas, and M. M. Rashidi, “A mathematical model of MHD nanofluid flow having gyrotactic microorganisms with thermal radiation and chemical reaction effects,” Neural Comput. Appl., 2018, doi: 10.1007/s00521-016-2768- 8.
K. R. Krishnanand, B. K. Panigrahi, P. K. Rout, and A. Mohapatra, “Application of multi-objective teaching-learning-based algorithm to an economic load dispatch problem with incommensurable objectives,” 2011, doi: 10.1007/978-3-642-27172- 4_82.
B. Majhi and G. Panda, “Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique,” Expert Syst. Appl., 2011, doi: 10.1016/j.eswa.2010.06.070.
T. N. Akudjedu et al., “A comparative study of segmentation techniques for the quantification of brain subcortical volume,” Brain Imaging Behav., 2018, doi: 10.1007/s11682-018-9835-y.
A. A. Aly, S. Bin Deris, and N. Zaki, “Research Review for Digital Image Segmentation Techniques,” Int. J. Comput. Sci. Inf. Technol., 2011, doi: 10.5121/ijcsit.2011.3509.
R. Yogamangalam and B. Karthikeyan, “Segmentation techniques comparison in image processing,” Int. J. Eng. Technol., 2013.
D. Karungan and N. Sujatha, “Survey on various image segmentation techniques,” JETIR1702025 J. Emerg. Technol. Innov. Res., 2017.
N. A.Ibraheem, R. Z. Khan, and M. M. Hasan, “Comparative Study of Skin Color based Segmentation Techniques,” Int. J. Appl. Inf. Syst., 2013, doi: 10.5120/ijais13-450985.
S. J. Nanda, G. Panda, and B. Majhi, “Improved identification of Hammerstein plants using new CPSO and IPSO algorithms,” Expert Syst. Appl., 2010, doi: 10.1016/j.eswa.2010.03.043.