Summary
The paper discusses an image segmentation algorithm based on Self-Organising Maps and its application for the improvement of hand recognition in a video sequence. The presented results were obtained as part of a larger project, which has an objective to build a training simulator for Ukrainian Sign Language. A particular emphasis in this research is made on the image preparation for Self-Organising Map training process for the purpose of successful recognition of image segments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akgül, C.B.: Cascaded self-organizing networks for color image segmentation (2004), http://www.tsi.enst.fr/~akgul/oldprojects/CascadedSOM_cba.pdf
Brown, D., Craw, I., Lewthwaite, J.: A SOM Based Approach to Skin Detection with Application in Real Time Systems, University of Aberdeen (2001), http://www.bmva.ac.uk/bmvc/2001/papers/33/accepted_33.pdf
Davydov, M.V., Nikolskyi, Y.V.: Automatic identification of sign language gestures by means on dactyl matching. Herald of National University “Lvivska Polytechnica” 589, 174–198 (2007)
Davydov, M.V., Nikolskyi, Y.V., Pasichnyk, V.V.: Software training simulator for sign language learning. Connection, 98–106 (2007) (in Ukrainian)
Davydov, M.V., Nikolskyi, Y.V., Pasichnyk, V.V.: Selection of an effective method for image processing based on dactyl matching for identification of sign language gestures. Herald of Kharkiv National University of Radio-Electronics 139, 59–68 (2008) (in Ukrainian)
Campbell, N.W., Thomas, B.T., Troscianko, T.: Neural Networks for the Segmentation of Outdoor Images. In: International Conference on Engineering Applications of Neural Networks, pp. 343–346 (1996)
Campbell, N.W., Thomas, B.T., Troscianko, T.: Segmentation of Natural Images Using Self-Organising Feature Maps, University of Bristol (1996)
Ford, A., Roberts, A.: Colour Space Conversions (1998), http://www.poynton.com/PDFs/coloureq.pdf
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Hodych, O., Nikolskyi, Y., Shcherbyna, Y.: Application of Self-Organising Maps in medical diagnostics. Herald of National University “Lvivska Polytechnica” 464, 31–43 (2002)
Hodych, O., et al.: Analysis and comparison of SOM-based training algorithms. Control Systems and Machines 2, 63–80 (2006) (in Ukrainian)
Hodych, O., et al.: High-dimensional data structure analysis using Self-Organising Maps. In: 9th International Conference, CAD Systems in Microelectronics. CADSM apos 2007, Feburary 2007, pp. 218–221 (2007)
Hoffmann, G.: CIE Color Space (2000), http://www.fho-emden.de/~hoffmann/ciexyz29082000.pdf
Hoffmann, G.: CIELab Color Space (2003), http://www.fho-emden.de/~hoffmann/cielab03022003.pdf
Hunt, R.W.G.: Measuring Colour, 3rd edn. Fountain Pr Ltd. (2001)
IBM Research Demonstrates Innovative Speech to Sign Language Translation System, Pressrelease (September 12, 2007), http://www-03.ibm.com/press/us/en/pressrelease/22316.wss
Moreira, J., Da Fontoura Costa, L.: Neural-based color image segmentation and classification using self-organizing maps (1996), http://mirror.impa.br/sibgrapi96/trabs/pdf/a19.pdf
Jiang, Y., Chen, K.-J., Zhou, Z.-H.: SOM Based Image Segmentation. LNCS (LNAI), vol. 2639, pp. 640–643. Springer, Heidelberg (2003)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)
Reyes-Aldasoro, C.C.: Image Segmentation with Kohonen Neural Network Self-Organising Maps (2004), http://www.cs.jhu.edu/~cis/cista/446/papers/SegmentationWithSOM.pdf
The iCommunicator User’s Guide (2005), http://www.myicommunicator.com/downloads/iCommunicator-UserGuide-v40.pdf
Wu, Y., Liu, Q., Huang, T.S.: An Adaptive Self-Organizing Color Segmentation Algorithm with Application to Robust Real-time Human Hand Localization. In: Proc. Asian Conf. on Computer Vision, Taiwan, (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hodych, O., Hushchyn, K., Shcherbyna, Y., Nikolski, I., Pasichnyk, V. (2009). SOM-Based Dynamic Image Segmentation for Sign Language Training Simulator. In: Yang, J., Ginige, A., Mayr, H.C., Kutsche, RD. (eds) Information Systems: Modeling, Development, and Integration. UNISCON 2009. Lecture Notes in Business Information Processing, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01112-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-01112-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01111-5
Online ISBN: 978-3-642-01112-2
eBook Packages: Computer ScienceComputer Science (R0)