Stereo rectification of uncalibrated and heterogeneous images

In this paper, an algorithm for rectifying heterogeneous and uncalibrated pairs of stereo images is presented. In particular, a pair of images is captured by using a combination of static and dynamic cameras at unequal zoom, thus having different focal lengths and/or image resolutions. The rectification of such pairs of images is made in two steps. In the first step, image shrinking based on focal ratios is performed for compensating the effect of unequal zoom levels followed by a zero padding on the smaller image for making the images of equal size. In the second step, rectification transformations are calculated by solving a nonlinear constrained optimization problem for a given set of pairs of corresponding points (SIFT descriptors) between stereo images. Experiments are performed to evaluate the performance of the proposed method and assess the improvements of the proposed method over direct rectification.

[1]  Charles Hansen,et al.  Rectification of images for binocular and trinocular stereovision , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[2]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[3]  Tim J. Dennis,et al.  Epipolar line estimation and rectification for stereo image pairs , 1996, IEEE Trans. Image Process..

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  M. Hebert,et al.  Applications of Non-Metric Vision to some Visually Guided Robotics Tasks , 1995 .

[6]  Emanuele Trucco,et al.  On robust rectification for uncalibrated images , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[7]  Soren W. Henriksen,et al.  Manual of photogrammetry , 1980 .

[8]  Briarcliff Manor.  Interactive Calibration of a PTZ Camera for Surveillance Applications , 2001 .

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

[10]  Stanley S. Ipson,et al.  Applications of a direct algorithm for the rectification of uncalibrated images , 2004, Inf. Sci..

[11]  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).

[12]  Nicholas Ayache,et al.  Trinocular Stereo Vision for Robotics , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Jie Zhou,et al.  Multiresolution and Wide-Scope Depth Estimation Using a Dual-PTZ-Camera System , 2009, IEEE Trans. Image Process..

[14]  A. Verri,et al.  A compact algorithm for rectification of stereo pairs , 2000 .

[15]  Reinhard Koch,et al.  A simple and efficient rectification method for general motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[16]  Emanuele Trucco,et al.  A compact algorithm for rectification of stereo pairs , 2000, Machine Vision and Applications.

[17]  Shree K. Nayar,et al.  Rectifying transformations that minimize resampling effects , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[18]  Yiannis Aloimonos.  Visual Navigation: From Biological Systems to Unmanned Ground Vehicles , 1996 .

[19]  Hsien-Huang P. Wu,et al.  Projective Rectification with Reduced Geometric Distortion for Stereo Vision and Stereoscopic Video , 2005, J. Intell. Robotic Syst..

[20]  Richard I. Hartley,et al.  Theory and Practice of Projective Rectification , 1999, International Journal of Computer Vision.

[21]  Yoshiaki Shirai,et al.  Three-Dimensional Computer Vision , 1987, Symbolic Computation.

[22]  Jie Zhou,et al.  Stereo vision using two PTZ cameras , 2008, Comput. Vis. Image Underst..