Phase Based Disparity Estimation Using Adaptive Structured Light and Dual-Tree Complex Wavelet

In this paper, we propose a phase-based approach to estimate disparity between stereo images using the Dual-Tree Complex Wavelet transform and adaptive structured light. Firstly, a random noise adaptive structured light pattern is projected onto objects and two cameras capture stereo images. The adaptive colors are acquired using principle component analysis in the RGB color space of the image of the scene under ambient light to maximize the energy of the structured light and meanwhile minimize the energy of other noise factors. A Dual tree Complex Wavelet transform is then applied on the original three RGB channels of the scene under adaptive structured light and a fourth channel which mainly contains projected random noise generated using inverse principle component analysis. Finally the disparity map between the stereo images is generated by locating the minimum phase differences between left and right complex wavelet coefficients. Our experimental results show the proposed approach can generate high quality disparity maps.

[1]  David J. Fleet,et al.  Phase-based disparity measurement , 1991, CVGIP Image Underst..

[2]  Aaron F. Bobick,et al.  Large Occlusion Stereo , 1999, International Journal of Computer Vision.

[3]  Joaquim Salvi,et al.  Pattern codification strategies in structured light systems , 2004, Pattern Recognit..

[4]  Juyang Weng,et al.  Image matching using the windowed Fourier phase , 1993, International Journal of Computer Vision.

[5]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[6]  Takeo Kanade,et al.  A Cooperative Algorithm for Stereo Matching and Occlusion Detection , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Mark R. Pickering,et al.  Dense depth estimation using adaptive structured light and cooperative algorithm , 2011, CVPR 2011 WORKSHOPS.

[8]  S. Birchfiled A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling , 1998 .

[9]  KweonIn So,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006 .

[10]  Margrit Gelautz,et al.  Local stereo matching using geodesic support weights , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Rafeef Abugharbieh,et al.  Registration of 2D to 3D joint images using phase-based mutual information , 2007, SPIE Medical Imaging.

[12]  Joaquim Salvi,et al.  Recent progress in coded structured light as a technique to solve the correspondence problem: a survey , 1998, Pattern Recognit..

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

[14]  N. Kingsbury Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[15]  W. Marsden I and J , 2012 .

[16]  Fangmin Shi,et al.  SSD Matching Using Shift-Invariant Wavelet Transform , 2001, BMVC.

[17]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..