Narrow Results

Disparity map estimation is often regarded as one of the most demanding operations in computer vision applications. Several algorithms have been proposed to solve this problem. With such a number of distinct approaches, the question of choosing the most suitable algorithm for a given application is often raised. Few and limited resources can be found in the literature covering this problem. In the following paragraphs it will be presented a comparative analysis of the performance and characteristics of a set of similarity measure algorithms proposed in the literature in the past few years. The obtained results can be regarded as an extremely valuable basis for selecting the most suitable registration algorithm for a given application. The study was focused on the analysis of two distinct aspects: the final matching error and the computational load of each of the considered correlation functions. Besides this comparative study, the advantages of using a pyramidal resolution approach were also considered. This scheme has proved to be effective in reducing the overall computation time and the required number of arithmetic operations, having an insignificant loss in the final matching error.