WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/10011056,
	  title     = {Logistic Model Tree and Expectation-Maximization for Pollen Recognition and Grouping},
	  author    = {Endrick Barnacin and  Jean-Luc Henry and  Jack Molinié and  Jimmy Nagau and  Hélène Delatte and  Gérard Lebreton},
	  country	= {},
	  institution	= {},
	  abstract     = {Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {14},
	  number    = {2},
	  year      = {2020},
	  pages     = {46 - 49},
	  ee        = {https://publications.waset.org/pdf/10011056},
	  url   	= {https://publications.waset.org/vol/158},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 158, 2020},
	}