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Syntactic Pattern Recognition in Computer Vision: A Systematic Review

Authors:

Gilberto Astolfi,

Fábio Prestes Cesar Rezende,

João Vitor De Andrade Porto,

Edson Takashi Matsubara,

Hemerson PistoriAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 3

Article No.: 65, Pages 1 - 35

https://doi.org/10.1145/3447241

Published: 17 April 2021 Publication History

Abstract

Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

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Index Terms

Syntactic Pattern Recognition in Computer Vision: A Systematic Review
1. Mathematics of computing
  1. Mathematical software
    1. Mathematical software performance

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 3

April 2022

836 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3461619

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 17 April 2021

Accepted: 01 January 2021

Revised: 01 November 2020

Received: 01 April 2020

Published in CSUR Volume 54, Issue 3

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https://doi.org/10.1109/JSEN.2023.3346880
Wang BLi MPeng ZLu W(2024)Hierarchical attributed graph-based generative façade parsing for high-rise residential buildingsAutomation in Construction10.1016/j.autcon.2024.105471164(105471)Online publication date: Aug-2024
https://doi.org/10.1016/j.autcon.2024.105471
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Serey JAlfaro MFuertes GVargas MDurán CTernero RRivera RSabattin J(2023)Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering ResearchSymmetry10.3390/sym1502053515:2(535)Online publication date: 17-Feb-2023
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