Browse Theses

Structure from motion (SFM), the problem of estimating 3D structure from 2D images hereof, is one of the most popular and well studied problems within computer vision. This thesis is a study within the area of SFM. The main objective of this work is to improve the robustness of the SFM algorithm so as to make it capable of tolerating a great number of outliers in the correspondences. For improving the robustness, a stereo image sequence is processed, so the random sampling algorithms can be employed in the structure and motion estimation. With this strategy, we employ Random Sample Consensus (RANSAC) in motion and structure estimation to exclude outliers. Since the RANSAC method needs the prior information about the scale of the inliers, we proposed an auto-scale RANSAC algorithm which determines the inliers by analyzing the probability density of the residuals. The experimental results demonstrate that SFM by the proposed auto-scale RANSAC is more robust and accurate than that by RANSAC. Another important contribution of this thesis is that we propose another novel and highly robust estimator: Kernel Density Estimation Sample Consensus (KDESAC) which employs Random Sample Consensus algorithm combined with Kernel Density Estimation (KDE). The main advantage of KDESAC is that no prior information and no scale estimators are required in the estimation of the parameters. The computational load of KDESAC is much lower than the robust algorithms which estimate the scale in every sample loop. The experiments on synthetic data show that the proposed method is more robust to the heavily corrupted data than other algorithms. KDESAC can tolerate more than 80% outliers and multiple structures. Although Adaptive Scale Sample Consensus (ASSC) can obtain such good performance as KDESAC, ASSC is much slower than KDESAC. KDESAC is also applied to SFM problem and multi-motion estimation with real data. The experiments demonstrate that KDESAC is robust and efficient.