Pollen grains contour analysis on verification approach

Authors:
Norma Monzón García;Víctor Alfonso Elizondo Chaves;Juan Carlos Briceño;Carlos M. Travieso
Affiliations:
Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Las Palmas, S ...;Computer Science Faculty, University of Costa Rica, San José, Costa Rica;Computer Science Faculty, University of Costa Rica, San José, Costa Rica;Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Las Palmas, S ...
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Year:
2012

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Abstract

Earth's biodiversity has been suffering the effects of human contamination, and as a result there are many species of plants and animals that are dying. Automatic recognition of pollen species by means of computer vision helps to locate specific species and through this identification, study all the diseases and predators which affect this specie, so biologist can improve methods to preserve this species. This work focuses on analysis and classification stages. A classification approach using binarization of pollen grain images, contour and feature extraction to locate the pollen grain objects within the images is being proposed. A Hidden Markov Model classifier was used to classify 17 genders and species from 11 different families of tropical honey bee's plants achieving a mean of 98.77% of success.