Automatic analysis of ocular focus detection based on visual features
dc.contributor.author | NASCIMENTO, D. O. | |
dc.contributor.author | OLIVEIRA, G. A. | |
dc.contributor.author | LOPES, Guilherme | |
dc.contributor.author | RODRIGUES, Paulo | |
dc.date.accessioned | 2019-08-17T20:00:30Z | |
dc.date.available | 2019-08-17T20:00:30Z | |
dc.date.issued | 2019 | |
dc.description.abstractalternative | The human eye focusing is one of the most important tasks in the cognitive process of scene interpretation. The ability to estimate the focusing regions may vary according to the used algorithm and the image being analyzed, bringing a satisfactory efficiency in a specific set of images. This paper studies 9 methods proposed in the last decade, using 21 different features, discovering relations between the information within the images and the efficiency of the prediction. Using a supervised database, this paper shows that dispersion features for intensity data and color are more significant for the method efficiency than those based only on the average of the data. Besides, this paper proposes and analyses the capacity of Machine Learning techniques in identifying patterns inside the original images and selecting the most appropriate method to estimate focusing points | en |
dc.description.abstractalternative | The human eye focusing is one of the most important tasks in the cognitive process of scene interpretation. The ability to estimate the focusing regions may vary according to the used algorithm and the image being analyzed, bringing a satisfactory efficiency in a specific set of images. This paper studies 9 methods proposed in the last decade, using 21 different features, discovering relations between the information within the images and the efficiency of the prediction. Using a supervised database, this paper shows that dispersion features for intensity data and color are more significant for the method efficiency than those based only on the verage of the data. Besides, this paper proposes and analyses the capacity of Machine Learning techniques in identifying patterns inside the original images and selecting the most appropriate method to estimate focusing points. | en |
dc.description.firstpage | 9396 | |
dc.description.lastpage | 9410 | |
dc.description.volume | 6 | |
dc.identifier.citation | NASCIMENTO, D. O.; OLIVEIRA, G. A.; LOPES, Guilherme.; RODRIGUES, Paulo. Automatic analysis of ocular focus detection based on visual features. Journal of Multidisciplinary Engineering Science and Technology (JMEST), v. 6, p. 9396, 2019. | |
dc.identifier.issn | 2458-9403 | |
dc.identifier.uri | https://repositorio.fei.edu.br/handle/FEI/1011 | |
dc.identifier.url | http://www.jmest.org/wp-content/uploads/JMESTN42352809.pdf | |
dc.relation.ispartof | Journal of Multidisciplinary Engineering Science and Technology (JMEST) | |
dc.rights | Acesso Aberto | |
dc.subject.otherlanguage | Automatic focus detection | en |
dc.subject.otherlanguage | Computational vision | en |
dc.subject.otherlanguage | Automatic focus detection | en |
dc.subject.otherlanguage | Focus analysis | en |
dc.subject.otherlanguage | Computational vision | en |
dc.title | Automatic analysis of ocular focus detection based on visual features | pt_BR |
dc.type | Artigo | pt_BR |
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