Accurately tuned search space in a feature based stereo matching

Computations in a feature based stereo matching which is basically used for depth extraction are generally very high. These computations essentially include feature extraction and matching which feature matching is usually higher. For a feature-based stereo matching, we accurately tune the search space based on some stereo imaging parameters like the focal length with pixels scale, the displacement of features and the maximum disparity. We show that results of previous matches can be used to narrow down the search space to find current match. We use directional derivative of disparity as a temporary concept to tune the search space accurately. Then we develop a fast feature based stereo algorithm based on the proposed search space tuning and non-horizontal thinned edge points as feature. The execution time of the proposed algorithm is lower than other methods. Moreover the matching rate is also higher.