An Approach to Correcting Image Distortion by Self Calibration Stereoscopic Scene from Multiple Views

An important step in the analysis and interpretation of video scenes for recognizing scenario is the aberration corrections introduced during the image acquisition in order to provide and correct real image data. This paper presents an approach on distortion correction based on stereoscopic self-calibration from images sequences by using a multi-camera system of vision (network cameras). This approach for correcting image distortion brings an elegant and robust technique with good accuracy. Without any knowledge of shooting conditions, the camera's parameters will be estimated. For this, the image key points of interest are extracted from different overlapping views of multi-camera system by local descriptor, matching is realized between the images, and then the fundamental matrix is estimated and rectified if necessary. It is finally possible to calculate the camera's extrinsic and intrinsic parameters. These geometric information of the camera are used as parameter's models of the distortion correction.

[1]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Stanley S. Ipson,et al.  Direct algorithm for rectifying pairs of uncalibrated images , 2000 .

[3]  Charles T. Loop,et al.  Computing rectifying homographies for stereo vision , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Kostas Daniilidis,et al.  A Unifying Theory for Central Panoramic Systems and Practical Applications , 2000, ECCV.

[5]  Sing Bing Kang,et al.  Catadioptric self-calibration , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  O. Faugeras,et al.  The Geometry of Multiple Images , 1999 .

[7]  Mohan M. Trivedi,et al.  Generalized Multiple Baseline Stereo and Direct Virtual View Synthesis Using Range-Space Search, Match, and Render , 2004, International Journal of Computer Vision.