A Stereo Matching Algorithm: An Adaptive WIndow Based on a Statistical Model

Abstract

This paper describes a stereo matching algorithm capable of selecting an appropriate window size to achieve both objectives of precise localization and stable estimation in scene correspondence. Window size is an important parameter that depends on two local attributes: local intensity variation and scene disparity variation. A statistical model is introduced for evaluating the impact of these two types of variations on the uncertainty of disparity estimation and proposes a method of selecting an appropriate window size to minimize the uncertainty of the estimation. Experiments have been conducted for various window sizes. The experimental results demonstrate the effectiveness of the proposed model and the matching algorithm with an adaptive window.