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| Rose,R. Reena; Meena,K.; Suruliandi,A.. |
| ABSTRACT Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN), capable of encoding texture patterns with pixels that lie at dissimilar regions, has been proposed recently to describe effective features for face images. However, the role of the descriptor can differ with different classifiers and distance metrics for diverse issues in face recognition. Hence, in this paper, an extensive evaluation of the LTDF_MLDN is carried out with an Extreme Learning Machine (ELM), a Support... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Face Recognition; Texture Descriptors; Local Texture Description Framework; Local Texture Description Framework-Based Modified Local Directional Number Pattern; Classifiers; Distance Metrics. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300405 |
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| Souza,Carlos H. Wachholz de; Mercante,Erivelto; Prudente,Victor H. R.; Justina,Diego D.D.. |
| C.H.W Souza, E. Mercante, V.H.R. Prudente and D.D.D. Justina. 2013. Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes. Cien. Inv. Agr. 40(2): 419-428. Satellite imagery, in combination with remote sensing techniques, provides a new opportunity for monitoring and assessing crops with lower cost and greater objectivity than traditional surveys. The present research employed Landsat 5/TM satellite imagery to identify the land cover classes in Cafelândia (Paraná, Brasil), a predominantly agricultural town. Five supervised classification methods (parallelepiped (PL), minimum distance (MND), Mahalanobis distance (MHD), maximum likelihood classifier (MLC) and spectral angle mapper (SAM))... |
| Tipo: Journal article |
Palavras-chave: Accuracy indices; Agricultural landscape; Classifiers; Remote sensing. |
| Ano: 2013 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000200016 |
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