Abstract : In this paper we propose a method of object classification based on the sequences of Zernike moments. The method makes use of the pattern recognition properties of Zernike moments and expands it to the problem of classification. Since the distinctive features of the classified objects are carried over to the Zernike moments, the proposed method allows for a robust, rotation and translation invariant classification of complex objects in grayscale images. In this approach, each object class has defined a reference Zernike moment sequence that is used as the prototype of the class. The object’s affiliation to the class is decided with the MSE criterion calculated for the object’s Zernike moments sequence and the reference Zernike moments sequence of the class. The method is tested using grayscale images of handwritten digits and microscopic sections.
Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.99-109, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_9〉
https://hal.inria.fr/hal-01656212
Contributeur : Hal Ifip
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Soumis le : mardi 5 décembre 2017 - 14:56:52
Dernière modification le : mercredi 6 décembre 2017 - 01:21:02
Aneta Górniak, Ewa Skubalska-Rafajłowicz. Object Classification Using Sequences of Zernike Moments. Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.99-109, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_9〉. 〈hal-01656212〉
