Paper Abstract

In this work a novel technique for selecting key points is proposed. Key points are used in many image processing applications and should be robust with respect to noise, rotation, blurring, and so on. The selection is based on the amount of local Fisher's information about location, orientation and scale. Based on the relationship between Taylor polynomials in Cartesian coordinates and Zernike polynomials in polar coordinates, the Fisher's information matrix can be written in terms of the image Zernike's expansion coefficients, which can be easily computed by means of a bank of filters. To evaluate the performances of the proposed method we consider four different distortions at three levels. Experimental results show that the performances, in terms of repeatability rate, are better that the performances obtained by the conventional Harris detector.