@inproceedings{lefebvrealbaret:08007:sign-lang:lrec, author = {Lefebvre-Albaret, Fran{\c c}ois and Gianni, Fr{\'e}d{\'e}rick and Dalle, Patrice}, title = {Toward an computer-aided sign segmentation}, pages = {123--128}, editor = {Crasborn, Onno and Efthimiou, Eleni and Hanke, Thomas and Thoutenhoofd, Ernst D. and Zwitserlood, Inge}, booktitle = {Proceedings of the {LREC2008} 3rd Workshop on the Representation and Processing of Sign Languages: Construction and Exploitation of Sign Language Corpora}, maintitle = {6th International Conference on Language Resources and Evaluation ({LREC} 2008)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marrakech, Morocco}, day = {1}, month = jun, year = {2008}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/08007.html}, abstract = {Processing sentences of a sign language corpus requires a first step of temporal segmentation, which is long and tedious. To realize this segmentation more quickly, we propose an innovating method of computer-aided segmentation. This method processes motions of the dominated and dominating hands during the sign realisation. The video treatments are applied in four steps. The first one consists in tracking the hands in a video sequence using particles filtering. Then, in a second step, an operator watches the video sequence and indicates for each sign a time stamp during the sign realisation. Using this information and the trajectories of each hand, our method is able to find the beginning and the end of each sign in a third step. At the end, the operator can eventually apply some rectifications and validate the segmentation. \par The presented article explains the different steps, from the calculation of the head and hands 2D positions to the computer-aided determination of the temporal segmentation of the signs. The segmentation exploits a model of French Sign Language and focuses especially on the characteristics of manual sign movements. Our method detects in the video several dynamic properties as the relative hands movement (symmetries, static hands) and the movement primitives (simple or double repetition, uniform or accelerated straight movement). We also detect time spaces between two consecutive signs. Those transitions must be economical, considering the necessary energy to realize these: a movement with a complex realization will contain a sign. Those elements are then combined to each other to determine the most plausible temporal segmentation of the signed sentences. The result can be represented as a succession of signs and transitions segments. \par Other observations can be taken into account to obtain the temporal segmentation. We can mention the determination of the elbows 2D positions, the characterization of hand configurations and the head orientation measurement. We describe how those elements could be used to improve the segmentation reliability. \par The proposed method is based on motion analysis and does not use any knowledge about the words used in the processed sentences. Using the characteristics shared by the majority of French Sign Language's signs, it is possible to detect not only standard signs but also other manual iconic signs. \par Our segmentation results are finally compared with a traditional manual segmentation produced with an annotation software named AnColin. This comparison exhibits several possible error sources. We focus on the problem of granularity and precision of the segmentation. We also discuss about other qualitative problems such as the detection criteria of the signs start and end. The evaluation protocol of a temporal segmentation is also adressed. Finally we will raise several problems to overcome, to realize a fully automatic segmentation.} } @inproceedings{dalle:06004:sign-lang:lrec, author = {Dalle, Patrice}, title = {High Level Models for Sign Language Analysis by a Vision System}, pages = {17--20}, editor = {Vettori, Chiara}, booktitle = {Proceedings of the {LREC2006} 2nd Workshop on the Representation and Processing of Sign Languages: Lexicographic Matters and Didactic Scenarios}, maintitle = {5th International Conference on Language Resources and Evaluation ({LREC} 2006)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Genoa, Italy}, day = {28}, month = may, year = {2006}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/06004.html}, abstract = {Sign language processing is often performed by processing each individual sign and most of existing sign language learning systems focus on lexical level. Such approaches rely on an exhaustive description of the signs and do not take in account the spatial structure of the sentence. We present a high level model of sign language that uses the construction of the signing space as a representation of both (part of) the meaning and the realization of a sentence. We propose a computational model of this construction and explain how it can be attached to a sign language grammar model to help analysis of sign language utterances and to link lexical level to higher levels. We describe the architecture of an image analysis system that performs sign language analysis by means of a prediction/verification approach. A graphical representation can be used to explain sentence construction.} } @inproceedings{aznar:06012:sign-lang:lrec, author = {Aznar, Guylhem and Dalle, Patrice}, title = {Analysis of the Different Methods to Encode {SignWriting} in Unicode}, pages = {59--63}, editor = {Vettori, Chiara}, booktitle = {Proceedings of the {LREC2006} 2nd Workshop on the Representation and Processing of Sign Languages: Lexicographic Matters and Didactic Scenarios}, maintitle = {5th International Conference on Language Resources and Evaluation ({LREC} 2006)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Genoa, Italy}, day = {28}, month = may, year = {2006}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/06012.html}, abstract = {This paper lists, evaluates and discuss the solutions to encode SW in Unicode. SignWriting is the most complex and popular writing formalism for sign languages. Unicode is the most popular encoding of characters aimed at unifying the various language-oriented encodings into a single format supporting every human language. This paper focuses on the first functional layer, which gives a correspondence between a SignWriting sign and a series of bytes. This is one of the prerequisites to represent a sign language electronically. The different possibilities to encode a given SignWriting sign are evaluated and compared on different criteria : the Unicode space requirements, the number of bytes the storage will require, the mathematical complexity and the side advantages offered. Keeping as much as possible of the information on how signs are written and entered, and offering capabilities to easily compare the symbols that compose these signs is also considered, so that the encoding can serve to study and compare how SignWriting is written. A reference encoding is then proposed, to serve as a basis for the next layers. Other bi-dimensional writing formalisms, currently not supported by Unicode, are considered to extend the presented work.} } @inproceedings{lenseigne:04017:sign-lang:lrec, author = {Lenseigne, Boris and Gianni, Fr{\'e}d{\'e}rick and Dalle, Patrice}, title = {A New Gesture Representation for Sign Language Analysis}, pages = {85--90}, editor = {Streiter, Oliver and Vettori, Chiara}, booktitle = {Proceedings of the {LREC2004} Workshop on the Representation and Processing of Sign Languages: From {SignWriting} to Image Processing. Information techniques and their implications for teaching, documentation and communication}, maintitle = {4th International Conference on Language Resources and Evaluation ({LREC} 2004)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Lisbon, Portugal}, day = {30}, month = may, year = {2004}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/04017.html}, abstract = {Computer aided human gesture analysis requires a model of gestures, and an acquisition system which builds the representation of the gesture according to this model. In the specific case of computer Vision, those representations are mostly based on primitives described from a perceptual point of view, but some recent issues in Sign language studies propose to use a proprioceptive description of gestures for signs analysis. As it helps to deal with ambiguities in monocular posture reconstruction too, we propose a new representation of the gestures based on angular values of the arm joints based on a single-camera computer vision algorithm.} } @inproceedings{aznar:04022:sign-lang:lrec, author = {Aznar, Guylhem and Dalle, Patrice}, title = {Computer Support for {SignWriting} Written Form of Sign Language}, pages = {109--110}, editor = {Streiter, Oliver and Vettori, Chiara}, booktitle = {Proceedings of the {LREC2004} Workshop on the Representation and Processing of Sign Languages: From {SignWriting} to Image Processing. Information techniques and their implications for teaching, documentation and communication}, maintitle = {4th International Conference on Language Resources and Evaluation ({LREC} 2004)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Lisbon, Portugal}, day = {30}, month = may, year = {2004}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/04022.html}, abstract = {Signwriting's thesaurus is very large. It consists of 425 basic symbols, split in 60 groups from 10 categories. Each basic symbol can have 4 different representations, 6 different fillings and 16 different spatial rotations.} } @inproceedings{borgia-etal-2012-resource:lrec, author = {Borgia, Fabrizio and Bianchini, Claudia S. and Dalle, Patrice and De Marsico, Maria}, title = {Resource production of written forms of Sign Languages by a user-centered editor, {SW}ift ({S}ign{W}riting improved fast transcriber)}, pages = {3779--3784}, editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do{\u g}an, Mehmet U{\u g}ur and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios}, booktitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Istanbul, Turkey}, day = {21--27}, month = may, year = {2012}, isbn = {978-2-9517408-7-7}, language = {english}, url = {https://aclanthology.org/L12-1210}, doi = {10.63317/5mxcr5ow296a}, abstract = {The SignWriting improved fast transcriber (SWift), presented in this paper, is an advanced editor for computer-aided writing and transcribing of any Sign Language (SL) using the SignWriting (SW). The application is an editor which allows composing and saving desired signs using the SW elementary components, called ``glyphs''. These make up a sort of alphabet, which does not depend on the national Sign Language and which codes the basic components of any sign. The user is guided through a fully automated procedure making the composition process fast and intuitive. SWift pursues the goal of helping to break down the ``electronic'' barriers that keep deaf people away from the web, and at the same time to support linguistic research about Sign Languages features. For this reason it has been designed with a special attention to deaf user needs, and to general usability issues. The editor has been developed in a modular way, so it can be integrated everywhere the use of the SW as an alternative to written ``verbal'' language may be advisable.} } @inproceedings{lefebvre-albaret-dalle-2010-video:lrec, author = {Lefebvre-Albaret, Fran{\c c}ois and Dalle, Patrice}, title = {Video Retrieval in Sign Language Videos : How to Model and Compare Signs?}, pages = {3049--3054}, editor = {Calzolari, Nicoletta and Choukri, Khalid and Maegaard, Bente and Mariani, Joseph and Odijk, Jan and Piperidis, Stelios and Rosner, Mike and Tapias, Daniel}, booktitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Valletta, Malta}, day = {17--23}, month = may, year = {2010}, isbn = {978-2-9517408-6-0}, language = {english}, url = {https://aclanthology.org/L10-1116}, doi = {10.63317/3satm5zceu3s}, abstract = {This paper deals with the problem of finding sign occurrences in a sign language (SL) video. It begins with an analysis of sign models and the way they can take into account the sign variability. Then, we review the most popular technics dedicated to automatic sign language processing and we focus on their adaptation to model sign variability. We present a new method to provide a parametric description of the sign as a set of continuous and discrete parameters. Signs are classified according to there categories (ballistic movements, circles ...), the symmetry between the hand movements, hand absolute and relative locations. Membership grades to sign categories and continuous parameter comparisons can be combined to estimate the similarity between two signs. We set out our system and we evaluate how much time can be saved when looking for a sign in a french sign language video. By now, our formalism only uses hand 2D locations, we finally discuss about the way of integrating other parameters as hand shape or facial expression in our framework.} } @inproceedings{braffort-etal-2004-toward:lrec, author = {Braffort, Annelies and Choisier, Annick and Collet, Christophe and Dalle, Patrice and Gianni, Fr{\'e}d{\'e}rick and Lenseigne, Boris and Segouat, J{\'e}r{\'e}mie}, title = {Toward an Annotation Software for Video of Sign Language, Including Image Processing Tools and Signing Space Modelling}, pages = {201--204}, editor = {Lino, Maria Teresa and Xavier, Maria Francisca and Ferreira, F{\'a}tima and Costa, Rute and Silva, Raquel}, booktitle = {4th International Conference on Language Resources and Evaluation ({LREC} 2004)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Lisbon, Portugal}, day = {24--30}, month = may, year = {2004}, isbn = {978-2-9517408-1-5}, language = {english}, url = {https://aclanthology.org/L04-1419}, doi = {10.63317/442zcqrzdvi9}, abstract = {Several French national projects have been achieved last years, allowing linguist and computer scientists working on French Sign Language (FSL) to establish a permanent collaboration. During these projects, several video FSL corpora have been realised. One of the research orientations induced by these projects relates to multi-disciplinary annotation. From the computer scientist's side, the aim is to develop an annotation software integrating different kind of tools based on image processing and 3d modelling that will be used to automatically annotate both lexical and syntactic information. This article presents several of these components. A first version of the annotation software integrating these components is under development.} }