Conference Paper

A kind of global motion estimation algorithm based on feature matching

Coll. of Autom., Harbin Eng. Univ., Harbin, China
DOI: 10.1109/ICMA.2009.5246379 Conference: Mechatronics and Automation, 2009. ICMA 2009. International Conference on
Source: IEEE Xplore

ABSTRACT In this paper, we propose a kind of global motion estimation algorithm based on feature matching. The scale invariant feature transform (SIFT) algorithm is applied to global motion estimation. The feature extracted by SIFT algorithm is invariant to image scale and rotation. The matching accuracy is very high even under the condition of additive noise, varying illumination and affine deformation. It is advantageous to get precise estimation. But the feature of local motion is disadvantageous for global motion estimation. In order to improve the accuracy of global motion estimation, an adaptive noise reduction algorithm is presented to eliminate local motion. The parameters of the camera affine model are computed by the least square method. The proposed algorithm is tested by the standard image sequences and compared with other related methods. The experiments show that the proposed algorithm is adaptive and more accurate.

0 Bookmarks
 · 
79 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Moving target extraction plays a prominent role in the whole target tracking process in image sequences with dynamic scene. This paper presents a new approach for moving target extraction. In order to automatically extract moving target and improve the accuracy, level set method is applied to extract moving target contour. Firstly, moving target is detected by difference algorithm. Secondly, Chan-Vese level set model is used to obtain the contour. The holes and discontinuous regions are filled through closing operation of mathematical morphology. According to the coordinates of the horizontal and perpendicular vertexes of binary image, the moving target is extracted. Finally, experiment results of the standard image sequences coastguard demonstrate that the proposed algorithm of this paper is highly adaptive and accurate.
    2012 Fourth International Conference on Computational and Information Sciences. 01/2010;
  • [Show abstract] [Hide abstract]
    ABSTRACT: In order to improve the real-time of global motion estimation, we proposed a adaptive global motion estimation method based on improved SUSAN algorithm and SIFT algorithm. According to the five matching results before the current matching, the method uses Kalman filter algorithm to predict overlap regions of the current matching two images, and then extracts feature points in the overlapping regions instead of the whole regions of the images. In the part of extracting feature points of the method, improving the SUSAN algorithm according to the geometric characteristics of the SUSAN templates, which improves the speed of extracting feature points. Writing code to implement the method in VS2008, and experimental verification: This method accelerates the executing speed of the algorithm ensuring the accuracy at the same time.
    Control and Decision Conference (CCDC), 2013 25th Chinese; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Based on time redundancy in video image sequences an adaptive SIFT (Scale-invariant feature transform) algorithm is proposed. According to the latest three models' outputs in global motion estimation, the algorithm predicts overlapping regions between reference and current frames by using Lagrange parabolic interpolation, and then extracts feature points in the smaller region instead of the whole image. In this way, it can eliminate a large number of information redundancies to increase the processing speed of each frame, improve the effectiveness of feature points and reduce the mismatch. Experimental results show that the improved algorithm has the features of strong adaptive ability, rapidity and high matching accuracy, and it can be applied to the real-time positioning.
    Intelligent Control and Automation (WCICA), 2012 10th World Congress on; 01/2012