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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using Grammars for Pattern Recognition in Images: A Systematic Review

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Dias Pedro, Ricardo Wandre [1] ; Nunes, Fatima L. S. [1] ; Machado-Lima, Ariane [1]
Total Authors: 3
[1] Univ Sao Paulo, Sch Arts Sci & Humanities, BR-03828000 Sao Paulo - Brazil
Total Affiliations: 1
Document type: Review article
Source: ACM COMPUTING SURVEYS; v. 46, n. 2 NOV 2013.
Web of Science Citations: 4

Grammars are widely used to describe string languages such as programming and natural languages and, more recently, biosequences. Moreover, since the 1980s grammars have been used in computer vision and related areas. Some factors accountable for this increasing use regard its relatively simple understanding and its ability to represent some semantic pattern models found in images, both spatially and temporally. The objective of this article is to present an overview regarding the use of syntactic pattern recognition methods in image representations in several applications. To achieve this purpose, we used a systematic review process to investigate the main digital libraries in the area and to document the phases of the study in order to allow the auditing and further investigation. The results indicated that in some of the studies retrieved, manually created grammars were used to comply with a particular purpose. Other studies performed a learning process of the grammatical rules. In addition, this article also points out still unexplored research opportunities in the literature. (AU)

FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support type: Research Projects - Thematic Grants
FAPESP's process: 10/15691-0 - Proposition, implementation and validation of techniques for virtual interactive medical training
Grantee:Fátima de Lourdes dos Santos Nunes Marques
Support type: Regular Research Grants