Abstract
Stereo matching is one of the most active research areas in computer vision for decades. The task of stereo matching is to find the point correspondence between two images of the same scene taken from different viewpoints. This paper presents a segment-based stereo matching algorithm. Firstly, the reference image is segmented using hill-climbing algorithm and local stereo matching is performed Scale Invariant Feature Transform (SIFT) feature points with Sum of Absolute Differences (SAD) block matching. Secondly, a set of reliable pixels is constructed by comparing the matching cost and the mutual cross-checking consistent between the left and right initial disparity maps, which can lead to an actual disparity plane. Thirdly, a set of all possible disparity planes are extracted and then plane fitting and neighboring segment merging are performed. Finally, the disparity planes are set in each region using graph cuts to obtain final disparity map. The evaluation of proposed algorithm on the Middlebury data set result shows that the proposed algorithm is competitive with state-of-the-art stereo matching algorithms.
Highlights
This paper is an extension of work originally presented in 15th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) [1]
As a result, improving the initial estimate of disparity has a direct impact on the final disparity estimation. This methodology has been tested on Middlebury test set and the results indicate that the proposed method is compatible with current stereo matching algorithms
The Scale Invariant Feature Transform (SIFT)-Sum of Absolute Differences (SAD) stereo matching algorithm improves the accuracy of disparity calculation
Summary
This paper is an extension of work originally presented in 15th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) [1]. The main challenge of stereo matching is to generate accurate disparity map by comparing corresponding pixels of the same scene taken from different viewpoints. Global algorithms [5, 6] are based on assumption of an explicit smoothness and exclude cost aggregation step, but consider the disparity solution based on minimization of the global cost function taking into account the entire image. These algorithms generally provide accurate and dense disparity measurements but the computational cost is very high.
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