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A novel method for invariant image reconstruction

Pawlak Mirosław, Panesar Gurmukh Singh, Korytkowski Marcin

Journal of Artificial Intelligence and Soft Computing Research

2021

Vol. 11, No. 1

69--80

In this paper we propose a novel method for invariant image reconstruction with the properly selected degree of symmetry. We make use of Zernike radial moments to represent an image due to their invariance properties to isometry transformations and the ability to uniquely represent the salient features of the image. The regularized ridge regression estimation strategy under symmetry constraints for estimating Zernike moments is proposed. This extended regularization problem allows us to enforces the bilateral symmetry in the reconstructed object. This is achieved by the proper choice of two regularization parameters controlling the level of reconstruction accuracy and the acceptable degree of symmetry. As a byproduct of our studies we propose an algorithm for estimating an angle of the symmetry axis which in turn is used to determine the possible asymmetry present in the image. The proposed image recovery under the symmetry constraints model is tested in a number of experiments involving image reconstruction and symmetry estimation.

Predict manoeuvring indices using AIS data by ridge regression

Mou J., Tang G., Rong H., Yue X.

Zeszyty Naukowe / Akademia Morska w Szczecinie

2013

nr 36 (108) z. 2

137--142

The ridge regression is presented for identify manoeuvring indices in Nomoto’s model, and the result indicates that the method is robust and does not rely on initial estimation. For selecting appropriate AIS data for manoeuvring indices predicting, a frequency domain identification method is presented.