A survey of graph grammars: theory and applications

Fahmy, Hoda; Blostein, Dorothea

doi:10.1109/icpr.1992.201776

Cited by 16 publications

(9 citation statements)

References 15 publications

(15 reference statements)

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“…The substitution of strings by multidimensional constructs introduces the important problem of embedding, as unlike with strings, there is no unique obvious way to select what and how parts of a graph can be changed to generate the next graph in a parse derivation. Several different strategies to represent the graphs, to restrict the graph categories to be used and to define the embedding transform have been proposed [18]. In [19] the use of pattern grammars expressed using predicate logic is suggested to improve accuracy in object detection.…”

Section: Related Workmentioning

confidence: 99%

A new strategy for applying grammatical inference to image classification problems

Pistori

Calway

Flach

2013

2013 IEEE International Conference on Industrial Technology (ICIT)

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This paper presents a new strategy to represent an image as a string so that standard grammar induction techniques can be used in computer vision problems. Two sets of experiments using an artificial and a real dataset have been conducted in order to explore the new strategy parameters and to have a first glimpse on its comparative performance against some standard machine learning techniques. The results are encouraging and the proposal opens new paths of exploration for syntactical pattern recognition.

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Section: Related Workmentioning

confidence: 99%

A new strategy for applying grammatical inference to image classification problems

Pistori

Calway

Flach

2013

2013 IEEE International Conference on Industrial Technology (ICIT)

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“…This shows an idea about interaction among physically distant symbols, which is semantically important. A quick survey about graph grammars is found in (285).…”

Section: Syntactic Approachesmentioning

confidence: 99%

Graphical Symbol Recognition

Santosh

Wendling

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic steps as well as prominent concepts or techniques and research standpoints/directions that are associated with graphical symbol recognition. Keywords. Graphics processing • graphical symbol • recognition, retrieval and spotting • statistical approaches • structural and syntactic approaches • real-world problems. '... the recurring wish for methods capable of efficiently combining structural and statistical methods' and 'the very structural and spatial nature of the information we work with makes structural methods quite natural in the community.' The problem can further be extended to symbol spotting, but one can view this as a kind of retrieval (29-33), which is guided by user queries. Additionally, the recognition/retrieval process can be made with the help of local descriptors like scale invariant feature transform (SIFT) and with the use of techniques like bag-of-features so that either primitive or region extraction (segmentation) can be avoided. The question always remains the same, 'what approach does what (i.e., performance) in which context?'. As mentioned earlier, graphics recognition has an extremely rich state-of-the-art literature in symbol recognition and localization (6; 9; 34; 35). But the major state-of-the-art methods for symbol recognition,

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“…As mentioned in the description of the grammar in section 4, grammars based on strings are not well suited for multi-threaded events, though for simple events they can be modeled by enumerating all the possible ordering among the threads. For complex multi-threaded events, graph grammars [5] or Petri-Nets can be used.…”

Section: Discussionmentioning

confidence: 99%

Attribute Grammar-Based Event Recognition and Anomaly Detection

Joo

Chellappa

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)

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We propose to use attribute grammars for recognizing normal events and detecting abnormal events in a video. Attribute grammars can describe constraints on features (attributes) in addition to the syntactic structure of the input. Events are recognized using an extension of the Earley parser that handles attributes and concurrent event threads. Abnormal events are detected when the input does not follow syntax of the grammar or the attributes do not satisfy the constraints in the attribute grammar to some degree. We demonstrate the effectiveness of our method for the task of recognizing normal events and detecting anomalies in a parking lot.

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A survey of graph grammars: theory and applications

Cited by 16 publications

References 15 publications

A new strategy for applying grammatical inference to image classification problems

A new strategy for applying grammatical inference to image classification problems

Graphical Symbol Recognition

Attribute Grammar-Based Event Recognition and Anomaly Detection

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