Research On Matching Algorithm And 3D Reconstruction Uncertainty From Two Images

Computer vision is the science of implementation of pattern recognition automatically in which computer finishes information processing and which is really a tremendous challenge during the process the fundamental and practical research of mankind.Image matching is most difficult and crucial in computer vision for which involves numerous problems. To understand factors and rules that influence the precision of reconstruction, to introduce 3D reconstruction uncertainty analysis is necessary. This paper presents the research of problems mentioned above.A fast images matching algorithm is proposed in this paper, which is based on gradient similarity and neighbor expansion. Interesting features (corners) are detected using SUSAN method and they are matched with improved Scott and Longuet-Higgins algorithm, the fundamental matrix is estimated after that. From the gradient field similarity of the two points, all pixels can be matched under the constraint of epipolar geometry. The neighbor expansion method and self-adapted neighbor selection strategy can handle the multi-candidate problem effectively and robust, rapid enough to be implemented unlike methods with continuity constraints and matching intensity algorithm. The algorithm is slightly effected by lighting environment and suitable for multi-scale images.Uniform precision analysis model is hard to achieve for large numbers of methods and tools used. Since the explicit uncertainty equation of 3D reconstruction achieved by error propagation theory is not intuitionistic, this paper presents a perturbation analysis model of 3D reconstruction and studies reconstruction precision affected by image digitalization error, matching error and calibration error using multidimensional analysis of statistic method. Gaussian noise is added to the reconstruction model for perturbation analysis using synthetic images, thus is helpful for uncertainty evaluation. Finally, expanded uncertainty of the reconstructed points is visualized. Perturbation analysis model and multidimensional analysis are more universal.Software of the matching algorithm and reconstruction uncertainty method is implemented and experiment results are given. Gross module design is introduced and details are presented for key problems. Experiments tested the effectiveness of the matching algorithm and important conclusions of reconstruction uncertainty problem are' brought.