Feature-Based Detection of Inverted-Stereo for Stereoscopic 3D Viewing Comfort

The inverted-stereo effect is a type of visual fatigue caused by reversed stereoscopy - i.e., the left-view image is delivered to the right eye and the right-view image to the left eye. Because the human visual system has no mechanism for recognizing inverted-stereo, the human brain struggles to overcome the oddness but ends up feeling strong visual discomfort. We propose a feature-based method for estimating the relative positions of stereo images. The proposed method is based on the properties of epipolar geometry and finds the relative positions from a pair of stereo images without prior knowledge of camera configurations. Furthermore, its computational complexity is relatively low because most of the required computation can be conducted by well-known linear algebraic operations.

[1]  Seong-Dae Kim,et al.  Analysis on the Spectrum of a Stereoscopic 3-D Image and Disparity-Adaptive Anti-Aliasing Filter , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[3]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[4]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[5]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.

[6]  Hakan Urey,et al.  State of the Art in Stereoscopic and Autostereoscopic Displays , 2011, Proceedings of the IEEE.

[7]  Ismo Rakkolainen,et al.  A Survey of 3DTV Displays: Techniques and Technologies , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Luc Van Gool,et al.  Fast scale invariant feature detection and matching on programmable graphics hardware , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[9]  Tom Drummond,et al.  Robust feature matching in 2.3µs , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[10]  Amit K. Roy-Chowdhury,et al.  Determining Topology in a Distributed Camera Network , 2007, 2007 IEEE International Conference on Image Processing.

[11]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Anton van den Hengel,et al.  Activity Topology Estimation for Large Networks of Cameras , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[13]  D. Makris.  Learning a Multi-camera Topology , 2003 .

[14]  Wa Wijnand IJsselsteijn,et al.  Visual discomfort in stereoscopic displays: a review , 2007, Electronic Imaging.

[15]  Thomas Wiegand,et al.  3D video: acquisition, coding, and display , 2010, IEEE Transactions on Consumer Electronics.

[16]  Xavier Armangué,et al.  Overall view regarding fundamental matrix estimation , 2003, Image Vis. Comput..

[17]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

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