In this paper, we investigate the confidence interval for the parameter of the common correlation coefficient of several bivariate normal populations. To do this, we use the confidence distribution approach. By simulation studies and using the concepts of coverage probability and expected length, We compare this method with the generalized variable approach. Results of simulation studies show that the coverage probability of the proposed method is close to the nominal level in all situations and also, in most cases, the expected length of this method is less than that of the generalized variable approach. Finally, we present two real examples to apply this approach.

Spatial processes are widely used in data analysis, specifically image processing. In image processing, examining periodic images is one of the most critical challenges. To investigate this issue, we can use periodically correlated spatial processes. To this end, it is necessary to determine whether the images are periodic or not, and if they are, what type of period it is. In the current study, we first introduce and express the properties of periodically correlated spatial processes. Then, we present a spatial periodogram to determine the period of periodically correlated spatial processes. Finally, we will elaborate on its usage to recognize the periodicity of the images.