Periodicity estimation in textured images using to ML approach

In the analysis of quasi-periodic textured images, good estimates of the periodicity parameters art instrumental to obtain an accurate evaluation of the texel characteristics. This paper presents a maximum likelihood algorithm for the estimation of periodicity parameters from the peaks of the localized Fourier spectrum. To derive the ML estimator, a stochastic model of the spectral peaks is required. Therefore, a probability model which incorporates false alarms, detection errors as well as random jitter in frequency estimates is described in this paper. The relationship between the ML algorithm proposed in this paper and the Hough transform is addressed. Finally, experimental tests with synthetic and natural images are presented to assess the performance of the ML estimator