Flexible Mirror Imaging

The field of view of a traditional camera has a fixed shape. This severely restricts how scene elements can be composed into an image. We present a novel imaging system that uses a flexible mirror in conjunction with a camera to overcome this limitation. By deforming the mirror, our system can produce fields of view with a wide range of shapes and sizes. A captured image is typically a multi-perspective view of the scene with spatially varying resolution. As a result, scene objects appear distorted. To minimize these distortions, we have developed an efficient algorithm that maps a captured image to one with almost uniform resolution. To determine this mapping we need to know the shape of the mirror. For this, we have developed a simple calibration method that automatically estimates the mirror shape from its boundary, which is visible in the captured image. We present a number of examples that demonstrate that a flexible field of view imaging system can be used to compose scenes in ways that have not been possible before. This flexibility can be exploited in applications such as video surveillance and monitoring.

[1]  M. Srinivasan,et al.  Reflective surfaces for panoramic imaging. , 1997, Applied optics.

[2]  Gilles Mathieu,et al.  Design of an autofocus lens for VGA ¼-in. CCD and CMOS sensors , 2004, SPIE Optical Systems Design.

[3]  Shree K. Nayar,et al.  A Theory of Single-Viewpoint Catadioptric Image Formation , 1999, International Journal of Computer Vision.

[4]  Kostas Daniilidis,et al.  Catadioptric camera calibration , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  Tomás Pajdla,et al.  Autocalibration & 3D reconstruction with non-central catadioptric cameras , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  Shree K. Nayar,et al.  Scene Collages and Flexible Camera Arrays , 2007, Rendering Techniques.

[7]  L. Szirmay-Kalos Virtual garments : A Fully Geometric Approach for Clothing Design , 2006 .

[8]  Sung Yong Shin,et al.  Image morphing using deformable surfaces , 1994, Proceedings of Computer Animation '94.

[9]  Brian A. Barsky,et al.  Reconstructing curved surfaces from specular reflection patterns using spline surface fitting of normals , 1996, SIGGRAPH.

[10]  John B. Moore,et al.  Resolution invariant surfaces for panoramic vision systems , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[12]  R. A. Hicks,et al.  Equiresolution catadioptric sensors. , 2005, Applied optics.

[13]  Francois Roddier,et al.  Adaptive Optics in Astronomy: Imaging through the atmosphere , 2004 .

[14]  Shree K. Nayar,et al.  A perspective on distortions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  Shree K. Nayar,et al.  Programmable Imaging: Towards a Flexible Camera , 2006, International Journal of Computer Vision.

[16]  Min Chen,et al.  Local Shape from Mirror Reflections , 2005, International Journal of Computer Vision.

[17]  R Andrew Hicks,et al.  Programmable imaging with two-axis micromirrors. , 2006, Optics letters.

[18]  Shree K. Nayar,et al.  The Raxel Imaging Model and Ray-Based Calibration , 2005, International Journal of Computer Vision.

[19]  Leonard McMillan,et al.  Modelling reflections via multiperspective imaging , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).