On fusion of range and intensity information using Graph-Cut for planar patch segmentation

Planar patch detection aims at simplifying data from 3D imaging sensors to a more compact scene description. We propose a fusion of intensity and depth information using Graph-Cut methods for this problem. Different known algorithms are additionally evaluated on low-resolution high-frame rate image sequences and used as an initialisation for the Graph-Cut approach. In experiments, we show a significant improvement of the detected patch boundaries after the refinement with our method.

[1]  Detecting coplanar feature points in handheld image sequences , 2007, VISAPP.

[2]  Wolfgang von Hansen.  ROBUST AUTOMATIC MARKER-FREE REGISTRATION OF TERRESTRIAL SCAN DATA , 2006 .

[3]  William J. Christmas,et al.  Mosaics from Arbitrary Stereo Video Sequences , 2004, DAGM-Symposium.

[4]  Robert Lange,et al.  3D time-of-flight distance measurement with custom solid-state image sensors in CMOS/CCD-technology , 2006 .

[5]  Daniel Freedman,et al.  Energy minimization via graph cuts: settling what is possible , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Hong Zhang,et al.  Planar patch extraction with noisy depth data , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[7]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  VekslerOlga,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001 .

[11]  Mubarak Shah,et al.  Motion layer extraction in the presence of occlusion using graph cuts , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.