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The Centering of the Invariant Feature for the Unfocused Input Character using a Spherical Domain System
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 Title & Authors
The Centering of the Invariant Feature for the Unfocused Input Character using a Spherical Domain System
Seo, Choon-Weon;
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TIn this paper, a centering method for an unfocused input character using the spherical domain system and the centering character to use the shift invariant feature for the recognition system is proposed. A system for recognition is implemented using the centroid method with coordinate average values, and the results of an above 78.14% average differential ratio for the character features were obtained. It is possible to extract the shift invariant feature using spherical transformation similar to the human eyeball. The proposed method, which is feature extraction using spherical coordinate transform and transformed extracted data, makes it possible to move the character to the center position of the input plane. Both digital and optical technologies are mixed using a spherical coordinate similar to the 3 dimensional human eyeball for the 2 dimensional plane format. In this paper, a centering character feature using the spherical domain is proposed for character recognition, and possibilities for the recognized possible character shape as well as calculating the differential ratio of the centered character using a centroid method are suggested.
Spherical Domain;Centering Character;Shift Invariant;Centroid;Differential Ratio;
 Cited by
Ramesh C. Jain, "Segmentation of Frame Sequences Obtained by a Moving Observer", IEEE vol.PAMI-6, no.5, pp.624-629, September, 1984.

Saburo Tsuji, Michiharu Osada and Masahiko Yachida, "Tracking and Segmentation of Moving Objects in Dynamic Line Images", IEEE vol.PAMI-2, no.6, pp.516-522, November, 1980.

Jong K. Won, Sang Y. Yi, Chung S Ryu, Seung H Lee and Eun S Kim, "Moving Target Segmentation using the Sequential Correlation", Proc. of KICS, vol.14, no.2, pp.947-950, 1995.

James H. Duncan and Tsai C Chou, "On the Detection of Motion and the Computation of Optical Flow", IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.14, no.3, pp.346-352, March, 1992. crossref(new window)

Y. N. Hsu and H. H. Arsenault, "Optical Pattern recognition using the Circular Harmonic Expansion", Appl. Opt. vol.21. pp.4016-4025, 1982. crossref(new window)

D. Casasent, S. F. Xia, A. J. Lee and J. Z. Jung, "Real-time Deformation Invariant Optical Pattern Recognition using Coordinate Transformations", Appl. Opt., vol.26, no.9, pp.938-942, 1987. crossref(new window)

C. Y Suen, "Character Recognition by computer and applications" in Handbook of Pattern Recognition and Image Processing", NewYork Academic Press, 1986.

Kyung-Hea Jang, Science of the Light, Sang hak dang, 2001.

Hecht, Optics 2ed, Addison-Wesley, 1996.


Skolnick M.M., Brown, R.H., Bhagvati, C., Wolf, B.R., "Morphological algorithms for centroid normalization in relational matching", IEEE International Symposium on Circuits and Systems, vol.2, 987-990, 1989.

Choon W. Seo, Sung. W. Ko and Byung S. Lee, "Image Character Recognition using the Mellin Transform and BPEJTC", J. of the KIIEE, vol.17, no.4, pp.26-35, 2003.