Improved seam carving for stereo image resizing

When stereo images are shown in three-dimensional (3D) display devices of different aspect ratios, the resizing algorithm for single image could lead to shape and depth distortion of the stereo image’s main content. This paper aims to propose a novel method for retargeting stereo image pairs without distorting important objects in the scene while still maintaining the consistency between the left and right images. We extended seam carving algorithm to stereo images. The novelty of our method is that important objects are determined by jointly considering the intensities of gradients and visual fusion area. The retargeted stereo pair has a feasible 3D interpretation that is similar to the original one. Our method protected the important content and reduced the visual distortion in each of the images as well as the depth distortion. Experimental results are presented to demonstrate that the proposed method effectively guaranteed the geometric consistency of resized stereo images.

[1]  Andreas Klaus,et al.  Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[3]  Jung-Young Son,et al.  A Stereoscopic Camera Model of Focal Plane Detector Array , 2011, Journal of Display Technology.

[4]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  J. Krol,et al.  Rehabilitation of a Classical Notion of Panum's Fusional Area , 1982, Perception.

[6]  Ariel Shamir,et al.  Seam Carving for Content-Aware Image Resizing , 2007, ACM Trans. Graph..

[7]  P. Panum.  Physiologische Untersuchungen über das Sehen mit zwei Augen , 1858 .

[8]  Shai Avidan,et al.  Geometrically consistent stereo seam carving , 2011, 2011 International Conference on Computer Vision.

[9]  Andrew Blake,et al.  Dense Stereo Matching over the Panum Band , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Christopher Joseph Pal,et al.  Learning Conditional Random Fields for Stereo , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Alan L. Yuille,et al.  Occlusions and Binocular Stereo , 1992, ECCV.

[12]  Takeshi Naemura,et al.  Seam carving for stereo images , 2010, 2010 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[13]  Nick Holliman,et al.  3D Display Systems , 2003 .