On the Relationship Between Active Contours and Contextual Classification
To discuss the relationship between active contours and contextual classification, a formal definition of the contour as well as a uniform approach to the all active contour methods are proposed first, and then a contextual classification problem is introduced and formalized. The equivalence relationship between contours and classifiers, thoroughly considered and illustrated by examples, proves to allow incorporation of the methods and techniques specific for the active contour approach to the contextual classification and vice versa.
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- 1.Kass M., Witkin W., Terzopoulos D., (1988) Snakes: Active Contour Models, International Journal of Computer Vision, 321–331Google Scholar
- 3.Xu Ch., Yezzi A., Prince J., (2000) On the Relationship between Parametric and Geometric Active Contours, in Proc. of 34th Asilomar Conference on Signals, Systems and Computers 483–489Google Scholar
- 4.Cootes T., Taylor C., Cooper D., Graham J., (1994) Active Shape Model-Their Training and Application, CVGIP Image Understanding, 61(1) 38–59 JanvierGoogle Scholar
- 6.Tadeusiewicz R., Flasinski M., (1991) Pattern Recognition, PWN, Warsaw (in Polish)Google Scholar
- 7.Kwiatkowski W., (2001) Methods of Automatic Pattern Recognition, WAT, Warsaw (in Polish)Google Scholar
- 8.Sobczak W., Malina W., (1985) Methods of Information Selection and Reduction, WNT, Warsaw (in Polish)Google Scholar
- 9.Nikolaidis N., Pitas I., (2001) 3-D Image Processing Algorithms, John Wiley and Sons Inc., New YorkGoogle Scholar
- 10.Bishop Ch., (1993) Neural Networks for Pattern Recognition, Clarendon Press, OxfordGoogle Scholar
- 11.Pal S., Mitra S., (1999) Neuro-fuzzy Pattern Pecognition, Methods in Soft Computing, John Wiley and Sons Inc., New YorkGoogle Scholar
- 12.Bennamoun M., Mamic G., (2002) Object Recognition, Fundamental and Case Studies, Springer-Verlag, LondonGoogle Scholar
- 13.Looney C., (1997) Pattern Recognition Using Neural Networks, Theory and Algorithms for Engineers and Scientists, Oxford University Press, New YorkGoogle Scholar
- 14.Sonka M., Hlavec V., Boyle R., (1994) Image Processing, Analysis and Machine Vision, Chapman and Hall, CambridgeGoogle Scholar
- 15.Gonzalez R., Woods R., (2002) Digital Image Processing, Prentice-Hall Inc., New JerseyGoogle Scholar