Local Structural Analysis: A Primer

Delalandre, Matthieu; Trupin, Eric; Ogier, Jean-Marc

doi:10.1007/978-3-540-25977-0_21

Cited by 7 publications

(10 citation statements)

References 49 publications

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“…The matrix can then be written as: [22,23]. Figure 9 illustrates the structure of an adjacency matrix M with regard to its row-column elements [24].…”

Section: Sign Language Recognition and Graph Matchingmentioning

confidence: 99%

A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network

Samir

Aboulela

Tolba

2014

The Egyptian Journal of Language Engineering

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Sign language recognition is one of the most challenging fields in Human-Computer Interface (HCI) applications. Although there are many obstacles that could dramatically limit the spread of sign language translators in our daily life, the community needs for these translators are no longer a luxury and increase day after day, other than the problems of sign languages all over the world, Arabic sign language enjoys its own difficulties and issues. This paper discusses Arabic sign language problems and proposes a recognition model for standard Arabic sign language. A model is proposed and developed for real-time hand signs recognition. The experiment was conducted on 100signs and the result was 94% recognition accuracy confirming words offline extendibility. Although the scientific understanding for the sign language is an essential step to build up a realistic recognition system, the proposed model can be used in other sign languages. The model exploits multi-stage Multiplicative Neural Networks for posture classification.

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“…The matrix can then be written as: [22,23]. Figure 9 illustrates the structure of an adjacency matrix M with regard to its row-column elements [24].…”

Section: Sign Language Recognition and Graph Matchingmentioning

confidence: 99%

A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network

Samir

Aboulela

Tolba

2014

The Egyptian Journal of Language Engineering

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“…With this application, we extract inclusion graphs which contain the external and the internal contour chains, and the inclusion links between the connected components. This method gives global/local descriptions of image's shapes [5]. The Fig.…”

Section: Vectorisation's Image Levelmentioning

confidence: 99%

“…Firstly, image is analyzed to find an entry point. The following tracking process is two types: line tracking and junction tracking [5]. The line tracking uses a point element, which advances into line's middle according to contours follow-up.…”

Section: Fig 2 (A) Contouring (B) Direct Vectorisation (C) Run (D) mentioning

confidence: 99%

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Adaptable Vectorisation System Based on Strategic Knowledge and XML Representation Use

Delalandre

Saidali

Trupin

et al. 2004

Graphics Recognition. Recent Advances and Perspectives

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Abstract. This paper presents a vectorisation system based on the use of strategic knowledge. This one is composed of two parts: a processing library and a graphic user interface. Our processing library is composed of image preprocessing and vectorisation tools. Our graphic user interface is used for the strategic knowledge acquisition and operationalisation. It allows to construct and to execute scenarios, exploiting any processing of our library, according to documents' contexts and users' adopted strategies. A XML data representation is used, allowing an easy data manipulation. A scenario example is presented for graphics recognition on utility maps.

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“…Into a related paper [5], we talk about "structural analysis" for this images processing step and propose a classification into local and global approaches. The boundary between the two approaches is the connected component.…”

Section: Structural Recognition Of Symbolsmentioning

confidence: 99%

Symbols Recognition System for Graphic Documents Combining Global Structural Approaches and Using a XML Representation of Data

Delalandre

Trupin

Ogier

2004

Lecture Notes in Computer Science

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Abstract. In this paper we present a symbols recognition system for graphic documents, based on a combination of global structural approaches. Our system allows to extract components and their loops, with their inclusion and neighboring links. So, it is possible to construct different graph types according to the considered recognition problem. Our system uses an XML representation of data, which allows an easy manipulation of these ones. We present some results on a symbols set of GREC2003's recognition contest.

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Local Structural Analysis: A Primer

Cited by 7 publications

References 49 publications

A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network

A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network

Adaptable Vectorisation System Based on Strategic Knowledge and XML Representation Use

Symbols Recognition System for Graphic Documents Combining Global Structural Approaches and Using a XML Representation of Data

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