research-article

Using grammars for pattern recognition in images: A systematic review

Authors:

Ricardo Wandré Dias Pedro,

Fátima L. S. Nunes,

Ariane Machado-LimaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 46, Issue 2

Article No.: 26, Pages 1 - 34

https://doi.org/10.1145/2543581.2543593

Published: 01 November 2013 Publication History

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Abstract

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.

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Reviewer: Markus Wolf

Grammars were used in computer science from the beginning for designing compilers for the first programming language. They were later used in image generation (for example, L-systems were used to generate beautiful pictures of plants). Therefore, using grammars for pattern recognition in images seems to be a natural research avenue. This paper represents an overview of papers and research directions concerning the use of grammars for pattern recognition in images over the last decade. After a short introduction of the field and a discussion of older reviews, the paper continues with an explanation of how the reviewed papers where sampled. The main body of the paper presents the review results. The results are stated quantitatively in the form of graphs and tables and are clustered according to similar techniques, objectives, or type of grammar used. A qualitative analysis section includes discussion of the different pattern recognition approaches in several short sections with references to the most important papers in the field. Following the results, the authors discuss the advantages and limits of the use of grammars for pattern recognition and point to future research directions. A short conclusion closes the paper. The paper is well written and gives a nice overview of the state of affairs with many useful references for in-depth study. This is a good starting point for anyone interested in techniques for pattern recognition in images. Due to the discussion of possible research directions, it is also good for anyone searching for research topics in this area. Online Computing Reviews Service

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ACM Computing Surveys Volume 46, Issue 2

November 2013

483 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2543581

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Publication History

Published: 01 November 2013

Accepted: 01 May 2013

Revised: 01 December 2012

Received: 01 March 2012

Published in CSUR Volume 46, Issue 2

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National Science and Technology Institute - Medicine Assisted by Computer Science (Instituto Nacional de Ciência e Tecnologia - Medicina Assistida por Computação Científica (INCT-MACC)

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