Masatoshi Okutomi

A multiple-baseline stereo

A stereo matching method that uses multiple stereo pairs with various baselines generated by a lateral displacement of a camera to obtain precise distance estimates without suffering from ambiguity is presented. Matching is performed simply by computing the sum of squared-difference (SSD) values. The SSD functions for individual stereo pairs are represented with respect to the inverse distance and are then added to produce the sum of SSDs. This resulting function is called the SSSD-in-inverse-distance. It is shown that the SSSD-in-inverse-distance function exhibits a unique and clear minimum at the correct matching position, even when the underlying intensity patterns of the scene include ambiguities or repetitive patterns. The authors first define a stereo algorithm based on the SSSD-in-inverse-distance and present a mathematical analysis to show how the algorithm can remove ambiguity and increase precision. Experimental results with real stereo images are presented to demonstrate the effectiveness of the algorithm. >

A locally adaptive window for signal matching

The authors presents a signal matching algorithm that can select an appropriate window size adaptively so as to obtain both precise and stable estimation of correspondences. A statistical model is presented for disparity variation within a window, and it is used to establish a link between the window size and the uncertainty of the computed disparity. This makes it possible to choose the window size that minimizes uncertainty in the disparity computed at each point. A theory is presented for the model and the resultant algorithm, together with analytical and experimental results that demonstrate their effectiveness.<<ETX>>

A stereo matching algorithm: An adaptive window based on a statistical model

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.