A Robust Stereo Matching Algorithm Based on …engold.ui.ac.ir/~p_moallem/Papers/Journals/P8-ARobust.pdfA Robust Stereo Matching Algorithm Based on Disparity Growing of Non-Horizontal
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A Robust Stereo Matching Algorithm Based on Disparity Growing of Non-Horizontal Edge Features
S.Shirazi1, P.Moallem2, M. Ashourian3
1Electrical Engineering, Tiran Branch, Islamic Azad University, Isfahan, Iran;
Email : [email protected] 2Electrical Engineering Department, Faculty of Engineering, University of Isfahan,
In this paper, a robust stereo matching technique using growing component seeds has been proposed in order to find a semi-dense disparity map. In the proposed algorithm only a fraction of the disparity space is checked to find a semi-dense disparity map. The method is a combination of both the feature based and the area based matching algorithms. The features are the non-horizontal edges extracted by the proposed feature detection algorithm. In order to compute the disparity values in non-feature points, a feature growing algorithm has been applied. The quality of correspondence seeds influences the computing time and the quality of the final disparity map. We show that the proposed algorithm achieves similar results as the ground truth disparity map. The proposed algorithm has been further compared with box filtering, belief propagation, random Growing-Component-Seeds and Canny-Growing-Component-Seeds. According to the obtained results, the proposed method increases the speed of computation ratio of the random GCS algorithm by 31% and the ratio of canny GCS algorithm by 13%. The experimental results show the proposed method achieves higher speed, more accurate disparity map, and the lowest RMS errors.
Keywords: stereo matching, feature based matching, area based matching, fast matching, Mathematics Subject Classification: Image Analysais 62H35
Computing Classification System:
1. INTRODUCTION
Stereo vision is an area of study that attempts to recreate the human vision system by using two or
more views of the same scene to derive 3D depth information about the scene. As an emerging
technology, stereo vision algorithms are constantly being revised and developed, and many
alternative approaches exist for implementing a stereo vision system (Munro, 2009), (Foggia 2007).
Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle
depth using stereo matching and disparity map. The disparity map or motion field obtained from the
The obtained results prove that the proposed method is fairly fast and robust against shadows.As it
is shown in Figures 5 to 13, BF and BP algorithms are area based algorithm, thus they operate
soundly in highly textured images as well as low textured images. The GCS algorithm achieves an
accurate disparity map, but due to the fact that the chosen growing points are randomly selected,
longer running time is elapsed in compare to the Canny+GCS algorithm and the NHE+GCS algorithm.
The Canny+GCS uses “canny” edge detector so that the pixels of horizontal edges are also grown,
thus it produces a disparity map with lower disparity than the NHE+GCS algorithm. According to
Table 2, the NHE+GCS algorithm is almost 31% faster than basic GCS algorithm, and the
Canny+GCS is 13% faster than basic GCS algorithm.
The NHE+GCS algorithm achieves higher speed and a more accurate disparity map with the
lowest RMS errors. As shown in Figure 13, the NHE+GCS algorithm works well for un-calibrated
(uncertified) real images as well. According to experimental results the NHE+GCS algorithm improves
the speed, accuracy, and robustness, simultaneously.
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5. CONCLUSION
In this paper a novel stereo matching algorithm has been proposed, namely the NHE+GCS
algorithm. Our algorithm consists of two phases; the first phase is feature extraction and feature
matching and the second one is disparity growing on a special neighborhood around features
extracted. The method combines feature and area based matching. The proposed algorithm has
been compared with box filtering, belief propagation, random Growing-Component-Seeds and canny
Growing-Component-Seeds. The box filtering algorithm based on area increases the accuracy while
the belief propagation increases the speed. The random and canny GCS algorithms increase both the
speed and accuracy but the proposed method increases the speed even more and meanwhile
preserves the accuracy. Thus, this method achieves a high quality disparity map in lower operation
time, suitable for obstacle detection which requires high speed and accuracy in computing the
disparity map.
6. REFERENCES
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